{"schema_version":"1.0","name":"ia-qa-toolbox","title":"IA-QA — 139+ QA & Dev Tools for AI Agents","description":"The open QA workbench for LLM stacks. 139+ testing primitives callable from Cursor, Claude Desktop, Windsurf, and any MCP agent. Prompt injection scan, hallucination check, semantic eval, RAG testing, VLM test suites, agent trajectory validation, guardrails, and classic dev tools (JSON, JWT, YAML, OpenAPI, encoding). No API key. No signup. Free.","homepage":"https://www.ia-qa.com","contact":"https://www.ia-qa.com/contactUs","attribution":"Powered by IA-QA (https://www.ia-qa.com) — free tools for developers & AI agents, by Jean-Christophe Jamet.","call_endpoint":"https://www.ia-qa.com/mcp/call","jsonrpc_endpoint":"https://www.ia-qa.com/mcp","discovery_hint":"This is a slim manifest. Use find_tool to search tools, GET /mcp/tool/{name} for full schema, or add ?full=1 to this URL for the complete manifest.","categories":["data","encoding","text","llm","qa","rag","dev","security","web","meta","eval"],"ecosystem":{"cli":{"name":"@ia-qa/cli","description":"Zero-dependency CLI — same 150+ tools from your shell. No signup, no API key.","install":"npm install -g @ia-qa/cli","usage":"ia-qa <tool> [input] [--flags]"},"self_healing":{"name":"@ia-qa/self-healing","description":"Local MCP server for UI test self-healing. Maps your app pages to role/name/selector contracts, diffs selector drift (PASS/FIX/BLOCK), and applies deterministic fixes to Cypress/Playwright/Selenium tests. Runs on your machine — nothing leaves it.","install":"npm install -g @ia-qa/self-healing","mcp_server":"ia-qa-heal-mcp","tools":["map_app","diff_mappings","fix_tests"],"requires":"Playwright (npx playwright install chromium)","cli_commands":["ia-qa-heal init","ia-qa-heal map","ia-qa-heal baseline","ia-qa-heal diff","ia-qa-heal fix"]}},"tools":[{"name":"format_json","category":"data","description":"Validate and pretty-print a string that is ALREADY valid JSON. Strict by design — it is a validity gate: valid JSON comes back formatted, anything else is rejected with the exact parse error. It never repairs, completes, or guesses. NOT for: plain text or prose (will fail), JSON embedded in markdown/prose (use extract_json_from_text first), JS objects (JSON.stringify them first), YAML (use yaml_to_json).","annotations":{"readOnlyHint":true,"destructiveHint":false,"idempotentHint":true,"openWorldHint":false},"schema_url":"https://www.ia-qa.com/mcp/tool/format_json","outputSchema":{"type":"object","properties":{"formatted":{"type":"string"},"valid":{"type":"boolean"}},"additionalProperties":true},"guidance":{"gate":"deterministic"}},{"name":"generate_uuid","category":"encoding","description":"Generate one or more cryptographically random UUID v4 identifiers. Use this when you need unique IDs for test fixtures, database records, session tokens, or any scenario requiring a guaranteed-unique string. Returns up to 100 UUIDs in one call.","annotations":{"readOnlyHint":true,"destructiveHint":false,"idempotentHint":false,"openWorldHint":false},"schema_url":"https://www.ia-qa.com/mcp/tool/generate_uuid","outputSchema":{"type":"object","properties":{"uuids":{},"count":{"type":"number"}},"additionalProperties":true}},{"name":"hash_text","category":"encoding","description":"Compute a cryptographic hash of a text string. Use when you need to verify data integrity, generate content fingerprints, hash passwords (prefer SHA-256+), or produce a fixed-length digest of any input. Supports SHA-256 (default), SHA-512, SHA-1, and MD5.","annotations":{"readOnlyHint":true,"destructiveHint":false,"idempotentHint":true,"openWorldHint":false},"schema_url":"https://www.ia-qa.com/mcp/tool/hash_text","outputSchema":{"type":"object","properties":{"hash":{},"algorithm":{},"input_length":{"type":"number"}},"additionalProperties":true}},{"name":"count_tokens","category":"llm","description":"Estimate the token count of a text string using the cl100k_base approximation (~4 chars/token). Call this BEFORE sending any text to an LLM API to check if it fits within the model context window and to estimate cost. Returns token estimate, character count, and word count.","annotations":{"readOnlyHint":true,"destructiveHint":false,"idempotentHint":true,"openWorldHint":false},"schema_url":"https://www.ia-qa.com/mcp/tool/count_tokens","outputSchema":{"type":"object","properties":{"tokens_estimate":{},"chars":{},"words":{}},"additionalProperties":true}},{"name":"base64_encode","category":"encoding","description":"Encode a UTF-8 string to Base64. Use when you need to embed binary data, multi-line text, or special characters safely inside JSON fields, HTTP headers, or data URIs.","annotations":{"readOnlyHint":true,"destructiveHint":false,"idempotentHint":true,"openWorldHint":false},"schema_url":"https://www.ia-qa.com/mcp/tool/base64_encode","outputSchema":{"type":"object","properties":{"encoded":{"type":"string"}},"additionalProperties":true}},{"name":"base64_decode","category":"encoding","description":"Decode a Base64 string back to UTF-8 text. Use for inspecting Base64-encoded API responses, JWT payload claims, config file values, or attachment data.","annotations":{"readOnlyHint":true,"destructiveHint":false,"idempotentHint":true,"openWorldHint":false},"schema_url":"https://www.ia-qa.com/mcp/tool/base64_decode","outputSchema":{"type":"object","properties":{"decoded":{}},"additionalProperties":true}},{"name":"url_encode","category":"encoding","description":"Percent-encode a string for safe use in URLs. Call this before programmatically building query strings, path segments, or form-encoded bodies to prevent injection and malformed URLs.","annotations":{"readOnlyHint":true,"destructiveHint":false,"idempotentHint":true,"openWorldHint":false},"schema_url":"https://www.ia-qa.com/mcp/tool/url_encode","outputSchema":{"type":"object","properties":{"encoded":{}},"additionalProperties":true}},{"name":"url_decode","category":"encoding","description":"Decode a percent-encoded URL string back to plain text. Use when parsing query parameters from raw URLs or when displaying encoded values to users.","annotations":{"readOnlyHint":true,"destructiveHint":false,"idempotentHint":true,"openWorldHint":false},"schema_url":"https://www.ia-qa.com/mcp/tool/url_decode","outputSchema":{"type":"object","properties":{"decoded":{}},"additionalProperties":true}},{"name":"generate_slug","category":"web","description":"Convert any string into a URL-friendly slug: lowercase, ASCII-normalized (é→e), special characters removed, spaces replaced with hyphens. Use for generating SEO-friendly URL paths, file names, or identifier keys from user-provided titles or labels.","annotations":{"readOnlyHint":true,"destructiveHint":false,"idempotentHint":true,"openWorldHint":false},"schema_url":"https://www.ia-qa.com/mcp/tool/generate_slug","outputSchema":{"type":"object","properties":{"slug":{}},"additionalProperties":true}},{"name":"validate_email","category":"dev","description":"Validate an email address against RFC 5322 syntax before storing it, sending a transactional email, or adding it to a mailing list. Returns { valid, email } — use this to avoid bounces and malformed data.","annotations":{"readOnlyHint":true,"destructiveHint":false,"idempotentHint":true,"openWorldHint":false},"schema_url":"https://www.ia-qa.com/mcp/tool/validate_email","outputSchema":{"type":"object","properties":{"valid":{"type":"boolean"},"email":{},"reason":{"type":"string"}},"additionalProperties":true},"guidance":{"gate":"deterministic"}},{"name":"minify_js","category":"dev","description":"Minify a JavaScript snippet, function, class, or module up to 50 KB using Terser. Returns minified code and byte savings. Use when embedding scripts in HTML templates, report payloads, or injecting inline code programmatically.","annotations":{"readOnlyHint":true,"destructiveHint":false,"idempotentHint":true,"openWorldHint":false},"schema_url":"https://www.ia-qa.com/mcp/tool/minify_js","outputSchema":{"type":"object","properties":{"minified":{}},"additionalProperties":true}},{"name":"decode_jwt","category":"dev","description":"Decode a JWT (JSON Web Token) and return its header and payload without verifying the signature. Also reports whether the token is expired and the exact expiry date. Use to inspect claims (sub, iss, exp, roles) during debugging or when integrating with an auth provider.","annotations":{"readOnlyHint":true,"destructiveHint":false,"idempotentHint":false,"openWorldHint":false},"schema_url":"https://www.ia-qa.com/mcp/tool/decode_jwt","outputSchema":{"type":"object","properties":{"header":{},"payload":{},"expired":{},"expiresAt":{},"note":{"type":"string"}},"additionalProperties":true}},{"name":"text_stats","category":"text","description":"Compute comprehensive statistics for any text: character count (with and without spaces), word count, line count, sentence count, paragraph count, and estimated reading time in minutes. Sentence counting is abbreviation-aware — titles (Mr., Dr.), acronyms (U.S., i.e., p.m.), initials, decimals, URLs and emails do not end a sentence, and a text with no terminal punctuation still counts as one. Use for validating form field lengths, evaluating LLM output verbosity, or content auditing.","annotations":{"readOnlyHint":true,"destructiveHint":false,"idempotentHint":true,"openWorldHint":false},"schema_url":"https://www.ia-qa.com/mcp/tool/text_stats","outputSchema":{"type":"object","properties":{"chars":{},"chars_no_space":{},"words":{},"lines":{},"sentences":{},"paragraphs":{},"reading_time_minutes":{}},"additionalProperties":true}},{"name":"generate_password","category":"encoding","description":"Generate a cryptographically secure random password using crypto.randomBytes. Configurable length (4–128), uppercase letters, digits, and symbols. Use when resetting user passwords, seeding test accounts, or generating API secrets.","annotations":{"readOnlyHint":true,"destructiveHint":false,"idempotentHint":false,"openWorldHint":false},"schema_url":"https://www.ia-qa.com/mcp/tool/generate_password","outputSchema":{"type":"object","properties":{"password":{},"length":{"type":"number"},"charset_size":{"type":"number"}},"additionalProperties":true}},{"name":"parse_csv","category":"data","description":"Parse a CSV string into a JSON array of objects (or raw arrays). Full RFC 4180: quoted fields may contain the delimiter, embedded newlines (the Excel/Sheets multi-line cell), and doubled quotes. Custom delimiters supported. An unterminated quote is rejected with its position rather than parsed into corrupted rows. Use when processing spreadsheet exports, data imports, or structured text pipelines where the source is CSV. Supports up to 200 KB.","annotations":{"readOnlyHint":true,"destructiveHint":false,"idempotentHint":true,"openWorldHint":false},"schema_url":"https://www.ia-qa.com/mcp/tool/parse_csv","outputSchema":{"type":"object","properties":{"rows":{"type":"array"},"headers":{"type":"array"},"columns":{"type":"number"},"row_count":{"type":"number"}},"additionalProperties":true}},{"name":"color_convert","category":"encoding","description":"Convert a color between HEX, RGB, and HSL formats. Use when translating design tokens between CSS notations, verifying color accessibility, or normalizing color values from user input. Accepts #rrggbb, #rgb, rgb(r,g,b), or hsl(h,s%,l%).","annotations":{"readOnlyHint":true,"destructiveHint":false,"idempotentHint":true,"openWorldHint":false},"schema_url":"https://www.ia-qa.com/mcp/tool/color_convert","outputSchema":{"type":"object","properties":{"input":{},"hex":{},"rgb":{"type":"string"},"hsl":{"type":"string"},"r":{},"g":{},"b":{}},"additionalProperties":true}},{"name":"regex_test","category":"text","description":"Test a regular expression pattern against an input string and return all matches with their index positions and named capture groups. Use for validating user inputs, extracting structured data from text, or debugging regex patterns. Supports flags g, i, m, s, u, y. The match runs in an isolated thread with a 500 ms budget: a pattern that blows up (catastrophic backtracking, e.g. \"(a+)+$\") comes back as redos_detected:true — a real ReDoS verdict on your pattern — instead of hanging.","annotations":{"readOnlyHint":true,"destructiveHint":false,"idempotentHint":true,"openWorldHint":false},"schema_url":"https://www.ia-qa.com/mcp/tool/regex_test","outputSchema":{"type":"object","properties":{"pattern":{},"flags":{},"matched":{},"match_count":{"type":"number"},"matches":{},"redos_detected":{"type":"boolean"},"elapsed_ms":{"type":"number"},"verdict":{},"note":{}},"additionalProperties":true}},{"name":"lorem_ipsum","category":"text","description":"Generate Lorem Ipsum placeholder text for UI mockups, design prototypes, or test data population. Configurable paragraphs (1–10), sentences per paragraph (1–20), and approximate words per sentence (3–30).","annotations":{"readOnlyHint":true,"destructiveHint":false,"idempotentHint":false,"openWorldHint":false},"schema_url":"https://www.ia-qa.com/mcp/tool/lorem_ipsum","outputSchema":{"type":"object","properties":{"paragraphs":{},"paragraph_count":{"type":"number"}},"additionalProperties":true}},{"name":"timestamp_convert","category":"dev","description":"Convert between Unix timestamps (seconds or milliseconds) and ISO-8601 / UTC date strings. Auto-detects epoch vs. millisecond format. Omit input to get the current time. Returns iso, unix_s, unix_ms, utc, date, and time fields.","annotations":{"readOnlyHint":true,"destructiveHint":false,"idempotentHint":false,"openWorldHint":false},"schema_url":"https://www.ia-qa.com/mcp/tool/timestamp_convert","outputSchema":{"type":"object","properties":{"iso":{"type":"string"},"unix_s":{"type":"number"},"unix_ms":{"type":"number"},"utc":{"type":"string"},"date":{"type":"string"},"time":{"type":"string"}},"additionalProperties":true}},{"name":"diff_text","category":"qa","description":"Compute a unified line-by-line diff between two text strings (LCS algorithm). Returns added/removed/unchanged line counts and formatted diff hunks with configurable context lines (0–20). Use to compare versions of prompts, configs, code snippets, or any text where you need to see exactly what changed.","annotations":{"readOnlyHint":true,"destructiveHint":false,"idempotentHint":true,"openWorldHint":false},"schema_url":"https://www.ia-qa.com/mcp/tool/diff_text","outputSchema":{"type":"object","properties":{"diff":{"type":"string"},"added":{"type":"number"},"removed":{"type":"number"},"unchanged":{"type":"number"}},"additionalProperties":true}},{"name":"truncate_to_tokens","category":"llm","description":"Truncate text to at most N tokens (cl100k_base: ~4 chars/token) to avoid exceeding an LLM context window. Optionally keeps the end of the text instead of the start (useful for keeping recent conversation history). Reports whether truncation occurred and the estimated token count.","annotations":{"readOnlyHint":true,"destructiveHint":false,"idempotentHint":true,"openWorldHint":false},"schema_url":"https://www.ia-qa.com/mcp/tool/truncate_to_tokens","outputSchema":{"type":"object","properties":{"text":{},"truncated":{"type":"boolean"},"tokens_estimate":{},"original_tokens_estimate":{}},"additionalProperties":true}},{"name":"split_chunks","category":"llm","description":"Split text into chunks of at most N tokens (cl100k_base: ~4 chars/token) with optional overlap. Designed for RAG ingestion pipelines.","annotations":{"readOnlyHint":true,"destructiveHint":false,"idempotentHint":true,"openWorldHint":false},"schema_url":"https://www.ia-qa.com/mcp/tool/split_chunks","outputSchema":{"type":"object","properties":{"chunks":{"type":"array"},"chunk_count":{"type":"number"},"tokens_per_chunk":{},"overlap_tokens":{}},"additionalProperties":true}},{"name":"extract_json_from_text","category":"llm","description":"Extract the first valid JSON object or array embedded in chaotic LLM output (surrounded by markdown fences, prose, or explanatory text). Handles ```json blocks and inline JSON. Call this whenever an LLM returns structured data mixed with explanation text instead of raw JSON.","annotations":{"readOnlyHint":true,"destructiveHint":false,"idempotentHint":true,"openWorldHint":false},"schema_url":"https://www.ia-qa.com/mcp/tool/extract_json_from_text","outputSchema":{"type":"object","properties":{"json":{},"source":{"type":"string"}},"additionalProperties":true}},{"name":"strip_markdown","category":"text","description":"Strip all Markdown formatting (headers, bold, italic, code fences, links, lists) from text and return clean plain text. Run this before injecting scraped documentation, README files, or user content into an LLM prompt to eliminate redundant markup tokens and reduce cost.","annotations":{"readOnlyHint":true,"destructiveHint":false,"idempotentHint":true,"openWorldHint":false},"schema_url":"https://www.ia-qa.com/mcp/tool/strip_markdown","outputSchema":{"type":"object","properties":{"text":{},"original_length":{"type":"number"},"stripped_length":{"type":"number"}},"additionalProperties":true}},{"name":"estimate_llm_cost","category":"llm","description":"Estimate the API cost in USD for a given model and token counts. Supports all major 2024–2026 models: GPT-4o, GPT-4.1, o3, o4-mini, Claude Opus 4, Claude Sonnet 4/4.5, Gemini 2.5 Pro/Flash, DeepSeek V3/R1, Grok 3, and legacy models.","annotations":{"readOnlyHint":true,"destructiveHint":false,"idempotentHint":true,"openWorldHint":false},"schema_url":"https://www.ia-qa.com/mcp/tool/estimate_llm_cost","outputSchema":{"type":"object","properties":{"model":{},"input_tokens":{},"output_tokens":{},"input_cost_usd":{"type":"string"},"output_cost_usd":{"type":"string"},"total_cost_usd":{"type":"string"},"rates":{"type":"object"}},"additionalProperties":true}},{"name":"escape_html","category":"text","description":"Escape HTML special characters (&, <, >, \", ') to their safe HTML entities. ALWAYS call this before inserting any user-provided or LLM-generated content into an HTML template to prevent cross-site scripting (XSS) attacks.","annotations":{"readOnlyHint":true,"destructiveHint":false,"idempotentHint":true,"openWorldHint":false},"schema_url":"https://www.ia-qa.com/mcp/tool/escape_html","outputSchema":{"type":"object","properties":{"escaped":{},"original_length":{"type":"string"}},"additionalProperties":true}},{"name":"unescape_html","category":"text","description":"Convert HTML entities (&amp;, &lt;, &gt;, &quot;, &#x27;, and numeric &#NNN;) back to plain characters. Use when processing HTML-encoded text from APIs, email content, or legacy database fields before passing to an LLM or displaying to users.","annotations":{"readOnlyHint":true,"destructiveHint":false,"idempotentHint":true,"openWorldHint":false},"schema_url":"https://www.ia-qa.com/mcp/tool/unescape_html","outputSchema":{"type":"object","properties":{"unescaped":{}},"additionalProperties":true}},{"name":"fetch_veille_feed","category":"web","description":"Fetch the latest QA & AI/LLM articles aggregated from curated RSS sources (Google Testing Blog, DEV.to Testing/QA/AI/LLM/Agents, Hugging Face Blog, Simon Willison). Perfect for agents monitoring the QA & AI landscape. Each article carries summary_source — the XML tag the summary was read from, or \"none\" when the feed publishes titles and links only; an empty summary with summary_source \"none\" is a property of that feed, not a parse failure.","annotations":{"readOnlyHint":true,"destructiveHint":false,"idempotentHint":false,"openWorldHint":true},"schema_url":"https://www.ia-qa.com/mcp/tool/fetch_veille_feed","outputSchema":{"type":"object","properties":{"articles":{"type":"array"},"total_found":{"type":"number"},"sources_queried":{"type":"number"},"category":{}},"additionalProperties":true}},{"name":"score_geo_signals","category":"web","description":"Analyze a webpage <head> HTML (or full HTML) for GEO (Generative Engine Optimization) signals. Returns a score /60 with per-check results and improvement tips. GEO = optimizing pages for AI-powered search engines (ChatGPT Search, Perplexity, etc.).","annotations":{"readOnlyHint":true,"destructiveHint":false,"idempotentHint":true,"openWorldHint":false},"schema_url":"https://www.ia-qa.com/mcp/tool/score_geo_signals","outputSchema":{"type":"object","properties":{"score":{},"max_score":{"type":"number"},"grade":{},"passed":{"type":"number"},"total_checks":{"type":"number"},"checks":{}},"additionalProperties":true},"guidance":{"gate":"signal-only"}},{"name":"extract_json_path","category":"data","description":"Extract a value from a JSON string using dot-notation path (e.g., \"user.address.city\", \"items.0.name\", \"meta.tags\"). Supports array index access via numeric path segments.","annotations":{"readOnlyHint":true,"destructiveHint":false,"idempotentHint":true,"openWorldHint":false},"schema_url":"https://www.ia-qa.com/mcp/tool/extract_json_path","outputSchema":{"type":"object","properties":{"value":{},"type":{},"path":{}},"additionalProperties":true}},{"name":"generate_json_ld","category":"data","description":"Generate a ready-to-paste <script type=\"application/ld+json\"> snippet for GEO / structured data optimization. Supported types: WebSite, FAQPage, Article, Person, Organization, SoftwareApplication, HowTo.","annotations":{"readOnlyHint":true,"destructiveHint":false,"idempotentHint":true,"openWorldHint":false},"schema_url":"https://www.ia-qa.com/mcp/tool/generate_json_ld","outputSchema":{"type":"object","properties":{"name":{"type":"string"},"acceptedAnswer":{"type":"object"},"snippet":{"type":"string"},"schema":{}},"additionalProperties":true}},{"name":"analyze_diff_bugs","category":"qa","description":"Pattern-based diff linter: flags a fixed set of risky shapes in changed code — query-string interpolation (SQL/Cypher/Mongo injection shape), shell interpolation, eval/new Function, empty catch blocks, regex built from a variable, fewer catch blocks than before, and named authorization guards that disappeared. Every finding cites the line that produced it. It does NOT do data-flow analysis: it cannot follow a value to a sink, across functions or files, and an empty result is not a safety verdict (the response lists what it did not analyse). Advisory triage — use a static analyser for a real security gate.","annotations":{"readOnlyHint":true,"destructiveHint":false,"idempotentHint":true,"openWorldHint":false},"schema_url":"https://www.ia-qa.com/mcp/tool/analyze_diff_bugs","outputSchema":{"type":"object","properties":{"totalSuggestions":{"type":"number"},"bugs":{"type":"array","items":{"type":"object"}},"overallRisk":{"type":"string"},"scannedLines":{"type":"number"},"rulesApplied":{"type":"number"},"notAnalysed":{"type":"array","items":{"type":"string"}},"disclaimer":{"type":"string"}},"additionalProperties":true},"guidance":{"gate":"signal-only","prefer":"a static analyser (CodeQL, Semgrep) for real data-flow — this is pattern matching on the diff, it cannot follow a value to a sink"}},{"name":"generate_test_cases","category":"qa","description":"Generate a set of test cases (valid, edge, invalid, pairwise) for a given feature description. Declared constraints drive the boundaries: a length or numeric bound (\"[8-64]\", \"min 8 chars\", \"at least 8 characters\" — read from inputs, and from the feature prose when a sentence names exactly one field) yields the last accepted value AND the first rejected one; a format (email/url/uuid, from the type, the field name or the wording) yields malformed-value cases. A bound nobody declared is labelled as this tool's assumption, not as expected behaviour. gherkinFormat renders every case (cap 200, stated in the output) and gherkinScenarioCount lets you check it against totalCases.","annotations":{"readOnlyHint":true,"destructiveHint":false,"idempotentHint":true,"openWorldHint":false},"schema_url":"https://www.ia-qa.com/mcp/tool/generate_test_cases","outputSchema":{"type":"object","properties":{"test_cases":{"type":"array","items":{"type":"object"}},"feature":{"type":"string"},"gherkinFormat":{"type":"string"},"gherkinScenarioCount":{"type":"number"},"parsedInputs":{"type":"array","items":{"type":"object"}}},"additionalProperties":true}},{"name":"run_pr_gate_pipeline","category":"qa","description":"Review triage for a pull request. Takes a unified git diff (`git diff HEAD`) and returns: diff-lint findings with the lines that produced them, regression impact areas, a risk score 0–100 with the factors that built it (churn, files touched, sensitive paths, whether any test file changed, lint severities, impacted risk areas), generated test cases, and a PASS / CONDITIONAL / BLOCK recommendation. Advisory: the score measures properties of the diff, not the correctness of the change — it does not read the code semantically and does not replace a reviewer or a static analyser. See notAnalysed in the response.","annotations":{"readOnlyHint":true,"destructiveHint":false,"idempotentHint":true,"openWorldHint":false},"schema_url":"https://www.ia-qa.com/mcp/tool/run_pr_gate_pipeline","outputSchema":{"type":"object","properties":{"mergeRecommendation":{},"riskScore":{},"riskLevel":{},"riskFactors":{"type":"array"},"inputFormat":{"type":"string"},"changedFiles":{},"bugsFound":{"type":"number"},"critical":{},"high":{},"testCasesGenerated":{},"impactAreas":{"type":"array"},"severityLevel":{},"sla":{},"topBugs":{"type":"array"},"notAnalysed":{"type":"array"},"disclaimer":{"type":"string"}},"additionalProperties":true},"guidance":{"gate":"signal-only","prefer":"secret_scan + run_eval_contract for the parts of a PR you can actually gate on"}},{"name":"validate_mcp_response","category":"qa","description":"Validate that an MCP tool response conforms to expected format, schema, and content rules. Use this to QA-test any MCP server tool. Supply the tool's actual JSON result and a set of checks to perform.","annotations":{"readOnlyHint":true,"destructiveHint":false,"idempotentHint":true,"openWorldHint":false},"schema_url":"https://www.ia-qa.com/mcp/tool/validate_mcp_response","outputSchema":{"type":"object","properties":{"passed":{"type":"number"},"failed":{"type":"number"},"total":{"type":"number"},"checks":{},"verdict":{"type":"string"}},"additionalProperties":true},"guidance":{"gate":"deterministic"}},{"name":"llm_output_validator","category":"qa","description":"Validate an LLM response against QA criteria: format checks (JSON, code, markdown), content rules (must-include, must-not-include), length constraints, language detection, and safety patterns. Essential for QA testing LLM-powered features.","annotations":{"readOnlyHint":true,"destructiveHint":false,"idempotentHint":true,"openWorldHint":false},"schema_url":"https://www.ia-qa.com/mcp/tool/llm_output_validator","outputSchema":{"type":"object","properties":{"passed":{},"failed":{},"total":{"type":"number"},"verdict":{},"checks":{}},"additionalProperties":true},"guidance":{"gate":"deterministic"}},{"name":"compare_responses","category":"qa","description":"Compare two ALREADY-PRODUCED outputs (e.g. model A vs model B on the same task) side by side. Returns deterministic metrics (token cosine, ROUGE-L, Jaccard, length/structure deltas, JSON diff) and a verdict. If a `reference` (ground truth) is given, scores each output against it and picks the closer one. If `model` + `api_key` are given, an LLM judge also picks a qualitative winner for the task. No re-execution — you bring the outputs.","annotations":{"readOnlyHint":true,"destructiveHint":false,"idempotentHint":true,"openWorldHint":false},"schema_url":"https://www.ia-qa.com/mcp/tool/compare_responses","outputSchema":{"type":"object","properties":{"labelA":{},"labelB":{},"metrics":{},"verdict":{},"judge":{},"summary":{"type":"string"}},"additionalProperties":true},"guidance":{"gate":"deterministic"}},{"name":"analyze_responses","category":"qa","description":"Semantically analyze N already-produced model outputs for the SAME task (the MCP counterpart to the LLM Sandbox). Without a reference: computes consensus — pairwise cosine agreement, the most-representative output, and the outlier. With a `reference` (ground truth): also ranks every output by closeness (token cosine + ROUGE-L composite) and names the closest. Deterministic, no LLM, no key — gate-able in CI. You bring the outputs (2+). For a 2-way head-to-head with structural JSON diff use compare_responses instead.","annotations":{"readOnlyHint":true,"destructiveHint":false,"idempotentHint":true,"openWorldHint":false},"schema_url":"https://www.ia-qa.com/mcp/tool/analyze_responses","outputSchema":{"type":"object","properties":{"count":{},"consensus":{},"reference_ranking":{},"summary":{"type":"string"}},"additionalProperties":true},"guidance":{"gate":"signal-only"}},{"name":"prompt_test_suite","category":"qa","description":"Define a test suite for a prompt: provide the system prompt, user prompt, and expected output criteria. Returns a test plan with scored rubric — use this as input for manual or automated LLM evaluation.","annotations":{"readOnlyHint":true,"destructiveHint":false,"idempotentHint":true,"openWorldHint":false},"schema_url":"https://www.ia-qa.com/mcp/tool/prompt_test_suite","outputSchema":{"type":"object","properties":{"test_suite_name":{"type":"string"},"total_tests":{"type":"number"},"categories":{"type":"array"},"rubric":{},"instructions":{"type":"string"}},"additionalProperties":true},"guidance":{"gate":"signal-only"}},{"name":"mcp_server_health_check","category":"qa","description":"Generate a health check report for an MCP server's tool manifest. Validates tool definitions, schema quality, naming conventions, and documentation completeness. Paste the server manifest JSON to audit.","annotations":{"readOnlyHint":true,"destructiveHint":false,"idempotentHint":true,"openWorldHint":false},"schema_url":"https://www.ia-qa.com/mcp/tool/mcp_server_health_check","outputSchema":{"type":"object","properties":{"passed":{"type":"number"},"failed":{"type":"number"},"total":{"type":"number"},"verdict":{"type":"string"},"checks":{"type":"array"},"toolIssues":{},"stats":{"type":"object"}},"additionalProperties":true},"guidance":{"gate":"deterministic"}},{"name":"mcp_server_evaluate","category":"qa","description":"Run a full compliance evaluation against a live MCP server URL. Tests: server reachability (ping), manifest discovery (GET /mcp), schema quality (snake_case names, descriptions, inputSchema), JSON-RPC 2.0 test call, and P50/P95 latency. 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Supports chat format (User/Assistant), XML tags, Markdown, or plain text.","annotations":{"readOnlyHint":true,"destructiveHint":false,"idempotentHint":true,"openWorldHint":false},"schema_url":"https://www.ia-qa.com/mcp/tool/few_shot_formatter","outputSchema":{"type":"object","properties":{"formatted":{},"example_count":{"type":"number"},"format":{},"token_estimate":{"type":"number"}},"additionalProperties":true}},{"name":"system_prompt_builder","category":"rag","description":"Build a structured system prompt from components: role, task, constraints, output format, tone, language, and examples. 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Covers 30+ models from OpenAI, Anthropic, Google, DeepSeek, Meta, Mistral, Cohere, xAI.","annotations":{"readOnlyHint":true,"destructiveHint":false,"idempotentHint":true,"openWorldHint":false},"schema_url":"https://www.ia-qa.com/mcp/tool/model_info","outputSchema":{"type":"object","properties":{"model":{},"pricing_per_1k":{"type":"object"}},"additionalProperties":true}},{"name":"compare_models","category":"llm","description":"Compare 2-5 AI models side by side: context window, pricing, multimodal, reasoning capabilities, and provider. 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Returns gate verdict (PASS/WARN/BLOCK), blockers, and actionable warnings. Use before merging any code change.","annotations":{"readOnlyHint":true,"destructiveHint":false,"idempotentHint":true,"openWorldHint":false},"schema_url":"https://www.ia-qa.com/mcp/tool/pr_gatekeeper","outputSchema":{"type":"object","properties":{"verdict":{"type":"string"},"score":{"type":"number"},"flags":{"type":"array","items":{"type":"string"}},"checks":{"type":"object"}},"additionalProperties":true},"guidance":{"gate":"deterministic"}},{"name":"list_llm_models","category":"llm","description":"List all LLM models available on ia-qa.com with their provider, API endpoint, and capabilities. Filter by provider name (e.g. \"Groq\", \"HuggingFace\", \"OpenAI\") or return the full catalog. 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(2) Default, no scores and no key — ranks with local BM25, a lexical keyword baseline: it answers \"does a keyword floor already surface my relevant passages?\", never \"is my neural reranker good\". (3) Live NVIDIA reranker — supply `api_key` for an NVIDIA account that still has reranking entitlement; NVIDIA retired its hosted reranking endpoints on 2026-05-18, so this one is for accounts that were grandfathered in.","annotations":{"readOnlyHint":true,"destructiveHint":false,"idempotentHint":false,"openWorldHint":true},"schema_url":"https://www.ia-qa.com/mcp/tool/rerank_evaluate","outputSchema":{"type":"object","properties":{"verdict":{"type":"string"},"precision_at_k":{"type":"number"},"recall_at_k":{"type":"number"},"k":{"type":"number"},"threshold":{"type":"number"},"has_ground_truth":{"type":"boolean"},"ranked":{"type":"array","items":{"type":"object"}},"total_passages":{"type":"number"},"mode":{"type":"string"},"model":{"type":"string"},"latency_ms":{"type":"number"}},"additionalProperties":true},"guidance":{"gate":"signal-only"}},{"name":"shield_analyze","category":"security","description":"Run a comprehensive AI guardrail analysis on an LLM response. 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Use this as a single-call safety gate before surfacing any LLM output to users.","annotations":{"readOnlyHint":true,"destructiveHint":false,"idempotentHint":false,"openWorldHint":true},"schema_url":"https://www.ia-qa.com/mcp/tool/shield_analyze","outputSchema":{"type":"object","properties":{"verdict":{},"score":{},"grade":{},"flags":{},"checks":{"type":"object"}},"additionalProperties":true},"guidance":{"gate":"signal-only","prefer":"gate on the sub-checks you can reproduce (checks.safety pii_scan, guardrail rules) rather than on the aggregate verdict — the score blends regex heuristics with an optional LLM judge"}},{"name":"security_headers_check","category":"security","description":"Analyse the HTTP security headers of a public URL OR of raw response headers you paste in. Grades each header (A–F) for: Strict-Transport-Security, Content-Security-Policy, X-Frame-Options, X-Content-Type-Options, Referrer-Policy, Permissions-Policy, X-XSS-Protection, Cross-Origin-Opener-Policy, Cross-Origin-Resource-Policy, and Cross-Origin-Embedder-Policy. Returns an overall score (0–100), per-header grades, missing headers, and fix snippets for Express, Nginx, and Apache. For localhost/private targets the remote server cannot reach, pass the `headers` parameter instead of `url`.","annotations":{"readOnlyHint":true,"destructiveHint":false,"idempotentHint":false,"openWorldHint":true},"schema_url":"https://www.ia-qa.com/mcp/tool/security_headers_check","outputSchema":{"type":"object","properties":{"header":{},"key":{},"value":{},"grade":{},"weight":{},"fix":{},"url":{},"source":{},"score":{},"overall_grade":{},"headers_checked":{"type":"number"},"missing_count":{"type":"number"},"weak_count":{"type":"number"},"missing":{},"weak":{},"details":{}},"additionalProperties":true},"guidance":{"gate":"deterministic"}},{"name":"ssl_certificate_check","category":"security","description":"Analyse the SSL/TLS certificate of any HTTPS host. Returns certificate subject, issuer, validity dates, days until expiry, protocol version, cipher suite, key exchange info, and an overall grade (A+, A, B, C, F). Detects expired, self-signed, and weak certificates. Use this to audit TLS posture before production deployment or during security reviews.","annotations":{"readOnlyHint":true,"destructiveHint":false,"idempotentHint":false,"openWorldHint":true},"schema_url":"https://www.ia-qa.com/mcp/tool/ssl_certificate_check","outputSchema":{"type":"object","properties":{"host":{"type":"string"},"grade":{"type":"string"},"subject":{"type":"object"},"issuer":{"type":"object"},"valid_from":{"type":"string"},"valid_to":{"type":"string"},"days_until_expiry":{"type":"number"},"is_expired":{"type":"boolean"},"is_self_signed":{"type":"boolean"},"protocol":{"type":"string"},"cipher":{"type":"object"},"issues":{"type":"array","items":{"type":"string"}}},"additionalProperties":true},"guidance":{"gate":"deterministic"}},{"name":"cors_test","category":"security","description":"Test a URL for CORS misconfigurations. 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A 4xx preflight IS a real result (the server refused it and a browser would fail closed).","annotations":{"readOnlyHint":true,"destructiveHint":false,"idempotentHint":false,"openWorldHint":true},"schema_url":"https://www.ia-qa.com/mcp/tool/cors_test","outputSchema":{"type":"object","properties":{"url":{},"risk_level":{"enum":["safe","low","medium","high","critical","unknown"]},"origins_tested":{"type":"number"},"origins_reachable":{"type":"number"},"origins_conclusive":{"type":"number"},"origins_inconclusive":{"type":"number"},"total_findings":{"type":"number"},"warning":{"type":"string"},"error":{"type":"string"},"tests":{}},"additionalProperties":true},"guidance":{"gate":"deterministic"}},{"name":"cors_checker","category":"security","description":"Check the CORS configuration of a URL the same way a browser would. Returns the main response status, all Access-Control-* headers, the tested origin, and the preflight OPTIONS response. 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Returns the newest requests first with method, headers, query params, body payload, and timestamps.","annotations":{"readOnlyHint":true,"destructiveHint":false,"idempotentHint":false,"openWorldHint":false},"schema_url":"https://www.ia-qa.com/mcp/tool/webhook_endpoint_requests","outputSchema":{"type":"object","properties":{"id":{"type":"string"},"request_count":{"type":"number"},"expires_at":{"type":"string"},"requests":{"type":"array"}},"additionalProperties":true}},{"name":"cookie_security_audit","category":"security","description":"Audit the security attributes of cookies set by any URL. Fetches the URL and inspects all Set-Cookie headers for: HttpOnly, Secure, SameSite, Domain scope, Path scope, Max-Age/Expires, __Host-/__Secure- prefixes. Flags insecure patterns: missing HttpOnly on session cookies, missing Secure flag, SameSite=None without Secure, overly broad Domain, and excessive TTL. Returns per-cookie grades and an overall security score (0–100) — the score is the WEAKEST cookie, not the average, so one leaking session cookie cannot be averaged into a green result (average_score is reported separately).","annotations":{"readOnlyHint":true,"destructiveHint":false,"idempotentHint":false,"openWorldHint":true},"schema_url":"https://www.ia-qa.com/mcp/tool/cookie_security_audit","outputSchema":{"type":"object","properties":{"url":{},"cookies_found":{"type":"number"},"score":{"type":"number"},"message":{"type":"string"},"cookies":{"type":"array"},"name":{},"httpOnly":{},"secure":{},"sameSite":{},"domain":{},"path":{},"max_age":{},"host_prefix":{},"secure_prefix":{},"issues":{}},"additionalProperties":true},"guidance":{"gate":"deterministic"}},{"name":"web_security_audit","category":"security","description":"Run a comprehensive web security audit combining headers, SSL, CORS, and cookies checks — then use an LLM to produce a prioritised remediation plan. Orchestrates security_headers_check + ssl_certificate_check + cors_test + cookie_security_audit in parallel, merges all findings, then asks an AI model to: (1) rank vulnerabilities by real-world exploitability, (2) generate a remediation roadmap, (3) produce fix code snippets for the detected stack. Returns both raw audit data and the AI analysis. Use this as a one-click security posture assessment.","annotations":{"readOnlyHint":true,"destructiveHint":false,"idempotentHint":false,"openWorldHint":true},"schema_url":"https://www.ia-qa.com/mcp/tool/web_security_audit","outputSchema":{"type":"object","properties":{"header":{},"key":{},"value":{},"grade":{},"weight":{},"fix":{},"url":{},"score":{},"overall_grade":{},"headers_checked":{"type":"number"},"missing_count":{"type":"number"},"weak_count":{"type":"number"},"missing":{},"weak":{},"details":{},"risk_level":{},"origins_tested":{"type":"number"},"total_findings":{"type":"number"},"tests":{},"cookies_found":{"type":"number"},"message":{"type":"string"},"cookies":{"type":"array"},"name":{},"httpOnly":{},"secure":{},"sameSite":{},"issues":{}},"additionalProperties":true},"guidance":{"gate":"deterministic"}},{"name":"secret_scan","category":"security","description":"Scan text or code for leaked secrets: API keys (AWS, GCP, Azure, OpenAI, Anthropic, Stripe, GitHub, GitLab, Slack, Twilio, SendGrid, HuggingFace), private keys (RSA/EC/PGP), JWTs, database connection strings, Bearer tokens, and Basic auth headers. Returns a list of findings with type, severity, line number, and a redacted preview. Use before committing code, sharing logs, or sending text to an LLM. 100% regex-based, zero network calls.","annotations":{"readOnlyHint":true,"destructiveHint":false,"idempotentHint":true,"openWorldHint":false},"schema_url":"https://www.ia-qa.com/mcp/tool/secret_scan","outputSchema":{"type":"object","properties":{"risk_level":{},"secrets_found":{},"findings_count":{"type":"number"},"findings":{},"input_lines":{"type":"number"},"scanned_types":{},"summary":{"type":"string"}},"additionalProperties":true},"guidance":{"gate":"deterministic"}},{"name":"similarity_score","category":"qa","description":"Compute text similarity between reference and hypothesis using multiple metrics: Cosine (BoW, TF-IDF), Jaccard, ROUGE-1, ROUGE-2, ROUGE-L, and BLEU. No API key needed. Ideal for LLM eval (expected vs actual), RAG quality checks, and NLG benchmarking. Supports batch mode.","annotations":{"readOnlyHint":true,"destructiveHint":false,"idempotentHint":true,"openWorldHint":false},"schema_url":"https://www.ia-qa.com/mcp/tool/similarity_score","outputSchema":{"type":"object","properties":{"precision":{"type":"number"},"recall":{"type":"number"},"f1":{"type":"number"},"mode":{"type":"string"},"count":{"type":"number"},"results":{}},"additionalProperties":true},"guidance":{"gate":"deterministic"}},{"name":"needle_haystack_generate","category":"eval","description":"Generate a \"needle in a haystack\" test: embeds a target fact into a large block of filler text at a specified position. Use this to test LLM context window retrieval accuracy. Returns the full haystack, the question to ask, and metadata. No API key needed.","annotations":{"readOnlyHint":true,"destructiveHint":false,"idempotentHint":false,"openWorldHint":false},"schema_url":"https://www.ia-qa.com/mcp/tool/needle_haystack_generate","outputSchema":{"type":"object","properties":{"haystack":{},"question":{},"needle":{},"position":{},"insert_block":{},"total_blocks":{"type":"number"},"estimated_tokens":{}},"additionalProperties":true}},{"name":"optimize_prompt_tokens","category":"llm","description":"Compress an LLM prompt by removing filler words, verbose phrases, duplicate sentences, and unnecessary whitespace. Returns optimized text with token savings breakdown. 100% deterministic, no API key needed.","annotations":{"readOnlyHint":true,"destructiveHint":false,"idempotentHint":true,"openWorldHint":false},"schema_url":"https://www.ia-qa.com/mcp/tool/optimize_prompt_tokens","outputSchema":{"type":"object","properties":{"optimized":{},"tokens_before":{},"tokens_after":{},"tokens_saved":{},"percent_saved":{"type":"string"},"steps":{}},"additionalProperties":true}},{"name":"bias_detect","category":"qa","description":"Analyse a set of LLM responses generated from the same prompt template but with different demographic variants (gender, origin, age, tone). Returns a bias score (0-100), sentiment analysis per variant, pairwise Jaccard similarity, and a human-readable verdict. No API key needed — runs entirely locally.","annotations":{"readOnlyHint":true,"destructiveHint":false,"idempotentHint":true,"openWorldHint":false},"schema_url":"https://www.ia-qa.com/mcp/tool/bias_detect","outputSchema":{"type":"object","properties":{"positive":{},"negative":{},"ratio":{},"biasScore":{},"verdict":{},"avgSimilarity":{"type":"string"},"minSimilarity":{"type":"string"},"sentimentVariance":{"type":"string"},"lengthCV":{"type":"string"},"sentiments":{},"pairwiseSimilarities":{}},"additionalProperties":true},"guidance":{"gate":"signal-only"}},{"name":"llm_fit_finder","category":"llm","description":"Find the best LLM for a given use case. Compares 30+ cloud API models and 12+ local models by cost, speed, benchmarks, features and VRAM requirements. Returns ranked recommendations with cost simulation. No API key needed.","annotations":{"readOnlyHint":true,"destructiveHint":false,"idempotentHint":true,"openWorldHint":false},"schema_url":"https://www.ia-qa.com/mcp/tool/llm_fit_finder","outputSchema":{"type":"object","properties":{"mode":{"type":"string"},"vram_gb":{},"quantization":{},"use_case":{},"results":{"type":"array"},"total_matching":{"type":"number"},"score":{"type":"number"},"tokens_per_day":{}},"additionalProperties":true},"guidance":{"gate":"signal-only"}},{"name":"diff_mappings","category":"qa","description":"Diff a baseline page mapping against a current one and return a CI-style verdict: PASS / FIX / BLOCK, plus per-element drift (ok, renamed, healable, ambiguous, lost, added, rebound). Pure and deterministic — provide two mappings as JSON with \"elements\" arrays of {role, name, selector, context?}. Use the companion @ia-qa/self-healing package (npm install -g @ia-qa/self-healing) to capture mappings from your app via its local MCP server ia-qa-heal-mcp, or paste the snippet from ia-qa.com/devtools/selector-drift into your browser console.","annotations":{"readOnlyHint":true,"destructiveHint":false,"idempotentHint":true,"openWorldHint":false},"schema_url":"https://www.ia-qa.com/mcp/tool/diff_mappings","outputSchema":{"type":"object","properties":{"verdict":{},"counts":{"type":"object"},"rows":{"type":"array"},"added":{"type":"array"}},"additionalProperties":true},"guidance":{"gate":"deterministic"}},{"name":"find_tool","category":"meta","description":"Search available MCP tools by keyword or category before calling them. Returns matching tool names, descriptions, and optionally their inputSchemas. Call this when you are unsure which tool to use or want to explore the catalogue. Categories: data, encoding, text, llm, qa, rag, dev, security, web.","annotations":{"readOnlyHint":true,"destructiveHint":false,"idempotentHint":true,"openWorldHint":false},"schema_url":"https://www.ia-qa.com/mcp/tool/find_tool","outputSchema":{"type":"object","properties":{"tool":{},"score":{},"query":{},"category":{},"count":{"type":"number"},"total_matches":{"type":"number"},"truncated":{"type":"boolean"},"tools":{"type":"array"},"hint":{}},"additionalProperties":true}},{"name":"list_local_tests","category":"eval","description":"Discover .ia-eval.yaml LLM test suite files in the project directory. Scans CWD and standard sub-directories (evals/, tests/, contracts/). Returns file paths ready to pass to run_eval_contract.","annotations":{"readOnlyHint":true,"destructiveHint":false,"idempotentHint":true,"openWorldHint":false},"schema_url":"https://www.ia-qa.com/mcp/tool/list_local_tests","outputSchema":{"type":"object","properties":{"files":{"type":"array","items":{"type":"string"}},"count":{"type":"number"},"dir":{"type":"string"}},"additionalProperties":true}},{"name":"run_eval_contract","category":"eval","description":"Parse a .ia-eval.yaml LLM test suite, call the specified LLM model for each scenario, run all configured scorers, and return a structured JSON report with per-scenario Pass/Fail verdicts and a Markdown summary. Use list_local_tests to discover available test files.","annotations":{"readOnlyHint":true,"destructiveHint":false,"idempotentHint":false,"openWorldHint":true},"schema_url":"https://www.ia-qa.com/mcp/tool/run_eval_contract","outputSchema":{"type":"object","properties":{"metadata":{"type":"object"},"contract_path":{},"warnings":{},"summary":{"type":"object"},"scenario_results":{}},"additionalProperties":true},"guidance":{"gate":"deterministic"}},{"name":"generate_html_report","category":"eval","description":"Convert a run_eval_contract() LLM Test Runner JSON result into a fully self-contained dark-themed HTML report with Pass/Fail badges, side-by-side Input/Output/Ground-Truth panels, evaluator score bars, and a radar chart. Returns the HTML as a string.","annotations":{"readOnlyHint":true,"destructiveHint":false,"idempotentHint":true,"openWorldHint":false},"schema_url":"https://www.ia-qa.com/mcp/tool/generate_html_report","outputSchema":{"type":"object","properties":{"html":{}},"additionalProperties":true}},{"name":"run_vlm_test_suite","category":"qa","description":"Run a test suite against a Vision-Language Model (VLM) — send an image (URL or base64) + N test cases (each with a question + assertion) to GPT-4o, Claude 3.5, or Gemini. Returns per-case PASS/FAIL verdicts, a pass rate, an overall PASS/WARNING/FAIL verdict (customizable threshold), and latency stats. Assertion types: contains, not_contains, json_format, min_length, max_length, semantic_contains (TF-IDF cosine similarity ≥ 0.4). BYOK: requires your own API key for the target provider.","annotations":{"readOnlyHint":true,"destructiveHint":false,"idempotentHint":false,"openWorldHint":true},"schema_url":"https://www.ia-qa.com/mcp/tool/run_vlm_test_suite","outputSchema":{"type":"object","properties":{"verdict":{"type":"string"},"passed":{"type":"number"},"failed":{"type":"number"},"total":{"type":"number"},"results":{"type":"array","items":{"type":"object"}},"model":{"type":"string"}},"additionalProperties":true},"guidance":{"gate":"deterministic"}},{"name":"multimodal_eval_guide","category":"qa","description":"Unified tool for multimodal AI evaluation: set action=guide for reference thresholds/interpretation (CLIP, FID, VQA), or set action=clip_score / fid_score / vqa_accuracy / pipeline to compute real metrics via HuggingFace Inference API and VLM BYOK calls. One tool for both reference and computation.","annotations":{"readOnlyHint":true,"destructiveHint":false,"idempotentHint":true,"openWorldHint":false},"schema_url":"https://www.ia-qa.com/mcp/tool/multimodal_eval_guide","outputSchema":{"type":"object","properties":{"metrics":{},"comparison_table":{"type":"array"},"best_practices":{"type":"array"},"web_tool":{"type":"string"},"score_interpretation":{},"results":{},"errors":{}},"additionalProperties":true}},{"name":"run_vlm_test_suite_batch","category":"qa","description":"Compare multiple VLMs on the same test suite in parallel — send an image (URL or base64) + N test cases to all models simultaneously. Returns per-model PASS/FAIL verdicts, pass rates, latency stats, and a comparison table. Assertion types: contains, not_contains, json_format, min_length, max_length, semantic_contains. BYOK: requires API keys for each provider.","annotations":{"readOnlyHint":true,"destructiveHint":false,"idempotentHint":false,"openWorldHint":true},"schema_url":"https://www.ia-qa.com/mcp/tool/run_vlm_test_suite_batch","outputSchema":{"type":"object","properties":{"verdict":{"type":"string"},"suites":{"type":"array","items":{"type":"object"}},"total_passed":{"type":"number"},"total_failed":{"type":"number"}},"additionalProperties":true},"guidance":{"gate":"deterministic"}},{"name":"generate_eval_yaml","category":"eval","description":"Generate a complete .ia-eval.yaml evaluation contract from a plain-language description of what your LLM should do. Uses Groq openai/gpt-oss-20b (server-side, no API key needed). Returns ready-to-run YAML for the LLM Test Runner (run_eval_contract). Picks appropriate evaluators (cosine_similarity, contains_check, hallucination_check, etc.) based on the task type.","annotations":{"readOnlyHint":true,"destructiveHint":false,"idempotentHint":false,"openWorldHint":true},"schema_url":"https://www.ia-qa.com/mcp/tool/generate_eval_yaml","outputSchema":{"type":"object","properties":{"yaml":{"type":"string"},"task_type":{"type":"string"},"scenario_count":{"type":"number"},"model_used":{"type":"string"}},"additionalProperties":true}},{"name":"generate_ci_workflow","category":"eval","description":"Generate a ready-to-commit GitHub Actions workflow that gates a build on IA-QA. Two gate types, combinable: \"eval_contract\" runs a .ia-eval.yaml through ia-qa-com/eval-action@v1 (LLM quality gate, needs a provider API key as a repo secret), and \"cli_checks\" runs deterministic primitives via npx @ia-qa/cli (secret scan, prompt-injection scan, security headers…) whose exit code fails the build. Deterministic template — no LLM call, no API key, same inputs give the same file. Returns the YAML, the secrets to create, and the remaining steps. Pair with generate_eval_yaml to produce the contract itself.","annotations":{"readOnlyHint":true,"destructiveHint":false,"idempotentHint":true,"openWorldHint":false},"schema_url":"https://www.ia-qa.com/mcp/tool/generate_ci_workflow","outputSchema":{"type":"object","properties":{"path":{"type":"string"},"yaml":{"type":"string"},"gate":{"type":"string"},"secrets_required":{"type":"array","items":{"type":"string"}},"notes":{"type":"array","items":{"type":"string"}},"next_steps":{"type":"array","items":{"type":"string"}}},"additionalProperties":true}},{"name":"validate_agent_trajectory","category":"eval","description":"Run declarative assertions on an agent trace (OpenAI tool-call messages, Anthropic tool_use/tool_result blocks, LangChain run trees, or plain text ReAct logs). No LLM call — deterministic. Assertion types: order (tool A before B), must_call, must_not_call, max_calls, min_calls, no_error, recovery (agent completes a successful step after its last error). A step counts as errored when the trace says so — is_error/isError, status/state in {error,failed,exception,…}, an error field, a JSON body with error/success:false — or when its text payload STARTS with an error marker (Error:, Traceback, TypeError:). Model prose is never scanned for keywords, and every errored step reports error_signal naming what flagged it. Returns per-assertion PASS/FAIL, parsed steps, warnings (a trace parsing to 0 steps is flagged — assertions passing on emptiness prove nothing), and an overall verdict. Use this to gate CI/CD on agent behavior correctness.","annotations":{"readOnlyHint":true,"destructiveHint":false,"idempotentHint":true,"openWorldHint":false},"schema_url":"https://www.ia-qa.com/mcp/tool/validate_agent_trajectory","outputSchema":{"type":"object","properties":{"verdict":{"type":"string"},"summary":{"type":"object","additionalProperties":true},"warnings":{"type":"array","items":{"type":"string"}},"results":{"type":"array","items":{"type":"object"}},"steps":{"type":"array","items":{"type":"object"}}},"additionalProperties":true},"guidance":{"gate":"deterministic"}},{"name":"run_semantic_tests","category":"eval","description":"Semantic assertion primitive: compare actual vs expected text pairs using cosine similarity + ROUGE-L. Two modes: tfidf (default, free, no API key) or embeddings (OpenAI text-embedding-3-small, BYOK, true semantic similarity). Returns per-case PASS/FAIL verdicts and an overall verdict. CI-ready: pipe the JSON verdict field to gate a build.","annotations":{"readOnlyHint":true,"destructiveHint":false,"idempotentHint":true,"openWorldHint":false},"schema_url":"https://www.ia-qa.com/mcp/tool/run_semantic_tests","outputSchema":{"type":"object","properties":{"verdict":{"type":"string"},"passed":{"type":"number"},"failed":{"type":"number"},"total":{"type":"number"},"results":{"type":"array","items":{"type":"object"}},"mode":{"type":"string"}},"additionalProperties":true},"guidance":{"gate":"deterministic"}},{"name":"metamorphic_check","category":"eval","description":"Reference-free stability primitive: instead of comparing an answer to a ground truth, it checks that an assistant's answer stays INVARIANT when the QUESTION is transformed (typo, casing, paraphrase, reordering, translation). Catches the failure class no reference answer can expose — an assistant that handles one phrasing well and a trivial variant of it badly. You bring the outputs (no model is called), so it is deterministic and free in tfidf mode. Relations: case (θ .95), typo (.90), paraphrase (.80), reorder (.80), translation (.75, embeddings only), specialization (.60, ADVISORY — directional, never gated). Returns PASS / FAIL / INVALID, where INVALID means the BASE answer was a refusal or too short so invariance was never measurable — an assistant that refuses every variant would otherwise score a perfect 1.0. Use run_semantic_tests alongside it: invariance without a correctness floor is a green light for a broken assistant.","annotations":{"readOnlyHint":true,"destructiveHint":false,"idempotentHint":true,"openWorldHint":false},"schema_url":"https://www.ia-qa.com/mcp/tool/metamorphic_check","outputSchema":{"type":"object","properties":{"verdict":{"type":"string"},"mode":{"type":"string"},"baseline":{"type":"object"},"summary":{"type":"object"},"variants":{"type":"array","items":{"type":"object"}},"weakest":{"type":"object"},"warnings":{"type":"array","items":{"type":"string"}},"thresholds":{"type":"object"}},"additionalProperties":true},"guidance":{"gate":"deterministic"}},{"name":"get_testing_guidelines","category":"eval","description":"Query the IA-QA methodology knowledge base. Returns structured testing guidelines, assertion strategies, thresholds, best practices, and relevant MCP tools for a given topic. Call without a topic to list all available topics. Topics: llm-unit-testing, rag-pipeline, prompt-stability, prompt-ab-testing, embedding-quality, eval-framework, semantic-testing, auto-testing, security, api-testing, ci-cd, multimodal, llm-data-security, agent-observability, pro-tips, learning-paths, golden-dataset.","annotations":{"readOnlyHint":true,"destructiveHint":false,"idempotentHint":true,"openWorldHint":false},"schema_url":"https://www.ia-qa.com/mcp/tool/get_testing_guidelines","outputSchema":{"type":"object","properties":{"available_topics":{"type":"array"},"usage":{"type":"string"},"keywords":{"type":"array"},"tip":{"type":"string"},"topic":{}},"additionalProperties":true}},{"name":"test_skill","category":"eval","description":"Validate a SKILL.md definition (Cursor / GitHub Copilot / Windsurf) by auto-generating trigger-positive and trigger-negative scenarios, running each through the model with the skill injected as a system prompt, and scoring trigger accuracy + step adherence. Returns a PASS/FIX/BLOCK verdict with per-scenario breakdown. Uses Groq llama-3.3-70b by default (server key, no api_key needed). Pass api_key + model to use your own provider.","annotations":{"readOnlyHint":true,"destructiveHint":false,"idempotentHint":false,"openWorldHint":true},"schema_url":"https://www.ia-qa.com/mcp/tool/test_skill","outputSchema":{"type":"object","properties":{"verdict":{"type":"string"},"score":{"type":"number"},"trigger_accuracy":{"type":"number"},"step_adherence":{"type":"number"},"scenarios":{"type":"array","items":{"type":"object"}}},"additionalProperties":true},"guidance":{"gate":"signal-only","prefer":"run_eval_contract for a reproducible, scored suite"}},{"name":"identify_caller","category":"meta","description":"Returns what the server knows about the current MCP client: clientInfo captured during initialize, User-Agent, and any _meta fields sent with this request. Useful for debugging caller identification.","annotations":{"readOnlyHint":true,"destructiveHint":false,"idempotentHint":true,"openWorldHint":false},"schema_url":"https://www.ia-qa.com/mcp/tool/identify_caller","outputSchema":{"type":"object","properties":{"session":{"type":"object"},"meta_override":{"type":"object"},"effective_agent":{"type":"string"},"note":{"type":"string"}},"additionalProperties":true}},{"name":"yaml_to_json","category":"data","description":"Parse a YAML string and return the equivalent JSON value. The reverse of json_to_yaml. Supports nested objects, arrays, anchors, aliases, multi-document streams, and all scalar types. Use when processing config files, CI/CD pipeline definitions, or OpenAPI specs authored in YAML.","annotations":{"readOnlyHint":true,"destructiveHint":false,"idempotentHint":true,"openWorldHint":false},"schema_url":"https://www.ia-qa.com/mcp/tool/yaml_to_json","outputSchema":{"type":"object","properties":{"documents":{},"count":{"type":"number"},"json":{}},"additionalProperties":true}},{"name":"env_parse","category":"dev","description":"Parse a .env file content into a JSON object. Handles quoted values (single and double), inline comments, export prefix, and escaped sequences (\\n, \\t inside double quotes). Returns all key-value pairs. Use in CI/CD pipelines, agent config loaders, or when processing dotenv files programmatically.","annotations":{"readOnlyHint":true,"destructiveHint":false,"idempotentHint":true,"openWorldHint":false},"schema_url":"https://www.ia-qa.com/mcp/tool/env_parse","outputSchema":{"type":"object","properties":{"vars":{},"count":{"type":"number"}},"additionalProperties":true}},{"name":"json_schema_generate","category":"data","description":"Infer a JSON Schema (draft-07) from a sample JSON value. Detects types, required fields, array item shapes, nested objects, and common string formats (email, uri, date, date-time, uuid). Returns a ready-to-use schema compatible with json_schema_validate. Use when you have a sample API response or LLM output and want to auto-generate a validation schema for CI/CD testing.","annotations":{"readOnlyHint":true,"destructiveHint":false,"idempotentHint":true,"openWorldHint":false},"schema_url":"https://www.ia-qa.com/mcp/tool/json_schema_generate","outputSchema":{"type":"object","properties":{"type":{"type":"string"},"format":{},"items":{"type":"object"},"schema":{"type":"object"}},"additionalProperties":true}},{"name":"format_table","category":"data","description":"Convert a JSON array of objects into a Markdown table. Automatically detects columns, aligns headers, and fills missing keys with empty cells. Use when an agent needs to present structured data — tool results, model comparisons, test reports — as a readable table in a response or document.","annotations":{"readOnlyHint":true,"destructiveHint":false,"idempotentHint":true,"openWorldHint":false},"schema_url":"https://www.ia-qa.com/mcp/tool/format_table","outputSchema":{"type":"object","properties":{"table":{"type":"string"},"rows":{"type":"number"},"columns":{"type":"number"}},"additionalProperties":true}},{"name":"openapi_validate","category":"dev","description":"Validate the structure of an OpenAPI 3.x specification (JSON or YAML). Checks required top-level fields (openapi, info.title, info.version, paths), validates each operation (responses, operationId uniqueness), detects undeclared $ref components, and flags missing 2xx responses. Returns a PASS/FAIL verdict, a 0–100 compliance score, and a list of errors and warnings with JSON-pointer locations. Use before publishing an API spec or generating SDK code.","annotations":{"readOnlyHint":true,"destructiveHint":false,"idempotentHint":true,"openWorldHint":false},"schema_url":"https://www.ia-qa.com/mcp/tool/openapi_validate","outputSchema":{"type":"object","properties":{"verdict":{},"score":{"type":"number"},"errors":{},"warnings":{},"stats":{"type":"object"}},"additionalProperties":true},"guidance":{"gate":"deterministic"}},{"name":"post_jira_comment","category":"integrations","description":"Post the output of jira_to_test_suite as a formatted comment on the source Jira ticket. Converts Gherkin, E2E steps, API tests, and ambiguities into Atlassian Document Format (ADF). STATEFUL — creates a comment on the issue.","annotations":{"readOnlyHint":false,"destructiveHint":false,"idempotentHint":false,"openWorldHint":false},"schema_url":"https://www.ia-qa.com/mcp/tool/post_jira_comment","outputSchema":{"type":"object","properties":{"success":{"type":"boolean"},"comment_id":{"type":"string"},"comment_url":{"type":"string"}},"additionalProperties":true}},{"name":"create_confluence_page","category":"integrations","description":"Create a new Confluence page from the output of jira_to_test_suite. Formats Gherkin, E2E steps, API tests, and test data as a properly structured Confluence page with code blocks and tables. STATEFUL — creates a new page in the specified space.","annotations":{"readOnlyHint":false,"destructiveHint":false,"idempotentHint":false,"openWorldHint":false},"schema_url":"https://www.ia-qa.com/mcp/tool/create_confluence_page","outputSchema":{"type":"object","properties":{"success":{"type":"boolean"},"page_id":{"type":"string"},"page_url":{"type":"string"},"title":{"type":"string"}},"additionalProperties":true}},{"name":"fetch_jira_issue","category":"integrations","description":"Fetch a complete Jira issue: summary, description converted to Markdown, status, assignee, priority, labels, custom fields, and optionally comments and attachment metadata. BYOK — credentials transit in-memory only, never stored on ia-qa.com.","annotations":{"readOnlyHint":true,"destructiveHint":false,"idempotentHint":false,"openWorldHint":true},"schema_url":"https://www.ia-qa.com/mcp/tool/fetch_jira_issue","outputSchema":{"type":"object","properties":{"key":{"type":"string"},"url":{"type":"string"},"summary":{"type":"string"},"status":{"type":"string"},"type":{"type":"string"},"priority":{"type":"string"},"description":{"type":"string"},"assignee":{"type":"string"},"reporter":{"type":"string"},"labels":{"type":"array","items":{"type":"string"}}},"additionalProperties":true}},{"name":"search_jira_issues","category":"integrations","description":"Search Jira using JQL (Jira Query Language). Returns matching issues with key fields. Ideal for finding open bugs, sprint tickets, or issues by label/assignee/component. BYOK — credentials transit in-memory only, never stored.","annotations":{"readOnlyHint":true,"destructiveHint":false,"idempotentHint":false,"openWorldHint":true},"schema_url":"https://www.ia-qa.com/mcp/tool/search_jira_issues","outputSchema":{"type":"object","properties":{"total":{"type":"number"},"returned":{"type":"number"},"jql":{"type":"string"},"issues":{"type":"array","items":{"type":"object"}}},"additionalProperties":true}},{"name":"jira_to_test_suite","category":"integrations","description":"Transform a Jira ticket into a complete test suite: Gherkin scenarios, E2E steps, API test cases, test data matrix, and ambiguity detection. Accepts either Jira credentials (auto-fetch) or a pre-fetched issue object. The returned test_suite includes _gherkin_warnings (deterministic syntax validation — empty if clean). Requires BYOK LLM key (OpenAI, Anthropic, etc.).","annotations":{"readOnlyHint":true,"destructiveHint":false,"idempotentHint":false,"openWorldHint":true},"schema_url":"https://www.ia-qa.com/mcp/tool/jira_to_test_suite","outputSchema":{"type":"object","properties":{"issue_key":{"type":"string"},"issue_url":{"type":"string"},"summary":{"type":"string"},"model_used":{"type":"string"},"latency_ms":{"type":"number"},"tokens_used":{"type":"number"},"test_suite":{"type":"object"}},"additionalProperties":true}},{"name":"fix_gherkin","category":"integrations","description":"Fix Gherkin syntax warnings from a jira_to_test_suite result. Takes the current gherkin text and the _gherkin_warnings array, calls your LLM to fix ONLY the flagged issues (adds missing Given/When/Then steps, etc.), and returns the corrected Gherkin. Lightweight — uses ~300-500 tokens vs ~5k for a full regeneration. Requires BYOK LLM key.","annotations":{"readOnlyHint":true,"destructiveHint":false,"idempotentHint":false,"openWorldHint":true},"schema_url":"https://www.ia-qa.com/mcp/tool/fix_gherkin","outputSchema":{"type":"object","properties":{"fixed_gherkin":{"type":"string"},"warnings_before":{"type":"number"},"warnings_after":{"type":"number"},"remaining_warnings":{"type":"array","items":{"type":"string"}},"model_used":{"type":"string"},"latency_ms":{"type":"number"}}}},{"name":"fetch_confluence_page","category":"integrations","description":"Fetch a Confluence page and return its content as clean Markdown. Accepts a numeric page_id or a full page URL. Optionally lists direct child pages. BYOK — credentials transit in-memory only, never stored.","annotations":{"readOnlyHint":true,"destructiveHint":false,"idempotentHint":false,"openWorldHint":true},"schema_url":"https://www.ia-qa.com/mcp/tool/fetch_confluence_page","outputSchema":{"type":"object","properties":{"page_id":{"type":"string"},"title":{"type":"string"},"markdown":{"type":"string"},"url":{"type":"string"},"children":{"type":"array","items":{"type":"object"}}},"additionalProperties":true}},{"name":"rate_tool","category":"meta","description":"Give honest usage feedback on an IA-QA MCP tool. Provide a score (1-5) and a comment. Rate low (1-2) if the tool was wrong, irrelevant, or a poor fit; rate high (4-5) only if it genuinely solved your need. Ratings are aggregated on a public dashboard at /devtools/mcp-ratings. Skip rating routine successes — we want signal, not praise. Example: rate_tool({ tool_name: \"format_json\", score: 2, comment: \"Tried to pretty-print a JSON5 file, it rejected trailing commas — not usable for my case.\" })","annotations":{"readOnlyHint":false,"destructiveHint":false,"idempotentHint":false,"openWorldHint":false},"schema_url":"https://www.ia-qa.com/mcp/tool/rate_tool","outputSchema":{"type":"object","properties":{"ok":{"type":"boolean"},"tool_name":{},"score":{},"comment":{},"truncated":{"type":"boolean"},"stored_chars":{"type":"number"},"rated_at":{"type":"string"},"message":{"type":"string"}},"additionalProperties":true}}]}