MCP server

IA-QA — 130+ QA & Dev Tools for AI Agents

io.github.JcJamet/ia-qa-toolbox

Checked 26 Oct 2026, 08:29 UTC · Connected to it and listed its tools

130+ QA & dev tools for AI agents: prompt injection, RAG testing, VLM eval, guardrails. Free.

Source

At a glance

Type

MCP server

Publisher

Not verified

Status

Working at last check

Tools

152

Token cost

About 38,666 tokens per request

Sets up in

Claude Code, Claude Desktop, Codex, Gemini CLI, Cursor, VS Code, Antigravity, ChatGPT, OpenAI API, Grok

Last checked

26 Oct 2026, 08:29 UTC

Connect it to your agent

Add to CursorAdd to VS CodeVS Code Insiders

Opens the app, which shows the server and asks you to confirm. For other apps, copy the setup below.

Claude Code

claude mcp add --transport http ia-qa-toolbox https://www.ia-qa.com/mcp

Claude Desktop

Settings → Connectors → Add custom connector, and paste the URL.

Codex (config.toml)

[mcp_servers.ia-qa-toolbox]
url = "https://www.ia-qa.com/mcp"

Gemini CLI

{"mcpServers":{"ia-qa-toolbox":{"httpUrl":"https://www.ia-qa.com/mcp"}}}

Cursor

{"mcpServers":{"ia-qa-toolbox":{"url":"https://www.ia-qa.com/mcp"}}}

VS Code

{"servers":{"ia-qa-toolbox":{"type":"http","url":"https://www.ia-qa.com/mcp"}}}

Antigravity

agy mcp add ia-qa-toolbox https://www.ia-qa.com/mcp

ChatGPT

Settings → Apps & Connectors → Advanced settings: turn on Developer mode, then Create a connector and paste the URL. Which plans can do this varies.

OpenAI API (Responses tools)

{"type":"mcp","server_label":"ia-qa-toolbox","server_url":"https://www.ia-qa.com/mcp","require_approval":"always"}

Grok (xAI API tools)

{"type":"mcp","server_label":"ia-qa-toolbox","server_url":"https://www.ia-qa.com/mcp"}

Tools

• ab_test_report ~250 tokens
  Generate an A/B test report comparing two prompts or model configurations. Accepts arrays of scores and returns statistical comparison: mean, median, std deviation, winner, and improvement percentage.
• analyze_diff_bugs ~295 tokens
  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 d
• analyze_responses ~377 tokens
  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 closen
• base64_decode ~94 tokens
  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.
• base64_encode ~96 tokens
  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.
• bias_detect ~233 tokens
  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 local
• bm25_score ~217 tokens
  Compute BM25 relevance score between a query and one or more documents. BM25 is the industry-standard keyword-based ranking algorithm used in Elasticsearch, OpenSearch, and Weaviate hybrid search. Returns ranked results with normalized scores.
• build_rag_prompt ~309 tokens
  Assemble a complete RAG (Retrieval-Augmented Generation) prompt from retrieved context chunks and a user query. Handles token budgeting, citation numbering, system instruction injection, and source attribution.
• calculate_readability ~105 tokens
  Calculate readability scores: Flesch Reading Ease, Flesch-Kincaid Grade Level, Coleman-Liau Index, and Automated Readability Index. Useful for evaluating LLM output quality.
• case_convert ~152 tokens
  Convert a string between naming conventions: camelCase, PascalCase, snake_case, kebab-case, UPPER_SNAKE_CASE, dot.case, Title Case. Essential for code generation and refactoring.
• check_contrast_ratio ~124 tokens
  Calculate WCAG 2.1 contrast ratio between two colors. Returns ratio and compliance for AA/AAA normal and large text.
• color_convert ~135 tokens
  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%).
• compare_models ~171 tokens
  Compare 2-5 AI models side by side: context window, pricing, multimodal, reasoning capabilities, and provider. Returns a comparison table with a recommendation based on your use case.
• compare_responses ~458 tokens
  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
• consistency_check ~241 tokens
  Compare multiple LLM responses to the same prompt and detect inconsistencies using Jaccard word-overlap similarity and fact drift (number comparison). Fast, deterministic, no API key needed. Limitations: relies on surface-level word matching — "Paris is the capital of France" vs "Paris is the French
• context_window_check ~190 tokens
  Given an array of message objects [{role, content}], estimate total token usage and check if it fits in the target model's context window. Warns about truncation risk.
• conversation_analyze ~144 tokens
  Analyze a multi-turn conversation for context retention, topic drift, instruction following, and repetition. Accepts messages array [{role, content}]. Essential for chatbot QA.
• cookie_security_audit ~228 tokens
  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, Same
• cors_checker ~183 tokens
  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. Use this for direct CORS debugging, not just security auditing.
• cors_test ~264 tokens
  Test a URL for CORS misconfigurations. Sends preflight (OPTIONS) requests with various Origin headers to detect: wildcard origins with credentials, origin reflection (echoing any origin), null origin acceptance, subdomain wildcard bypass, and missing Vary headers. Returns risk level (safe/low/medium
• cot_analyzer ~157 tokens
  Analyze a Chain-of-Thought (CoT) or reasoning trace from an LLM. Detects step count, logical flow, conclusion presence, backtracking, and estimates reasoning depth. Useful for o1/o3/DeepSeek-R1 evaluation.
• count_code_lines ~145 tokens
  Count lines of code: total, code lines, comment lines, blank lines, and comment density. Supports JS/TS, Python, Java/C/C++, Ruby, Go, Shell, HTML/XML, and CSS.
• count_tokens ~129 tokens
  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.
• create_confluence_page ~381 tokens
  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.
• cron_parse ~100 tokens
  Parse a cron expression into a human-readable schedule description. Supports standard 5-field cron (minute hour day month weekday).
• cron_validator ~161 tokens
  Validate a 5-field cron expression, explain the schedule, and preview the next execution times. Use this to debug cron jobs before they reach production. Returns parsed fields, a human-readable description, and upcoming ISO timestamps.
• decode_jwt ~135 tokens
  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.
• detect_language ~132 tokens
  Detect the natural language of a text using n-gram frequency analysis and common word markers. Supports 15 languages: English, French, Spanish, German, Italian, Portuguese, Dutch, Russian, Chinese, Japanese, Korean, Arabic, Polish, Turkish, Swedish.
• detect_secrets ~153 tokens
  Scan code or config files for hardcoded secrets: AWS keys, GitHub tokens, OpenAI/Anthropic API keys, Stripe secrets, JWTs, database connection strings, and generic passwords. Returns findings with severity. Run before every commit.
• diff_mappings ~271 tokens
  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
• diff_text ~179 tokens
  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 change
• embedding_similarity ~322 tokens
  Compute text similarity using local algorithms (Bag of Words, TF-IDF, Character N-grams). No API key needed — runs entirely in-process. NOT real embeddings: for true semantic similarity with vector embeddings, use run_semantic_tests with mode="embeddings" and your OpenAI API key. Supports single pai
• env_parse ~145 tokens
  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.
• escape_html ~113 tokens
  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.
• estimate_llm_cost ~186 tokens
  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.
• extract_json_from_text ~144 tokens
  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.
• extract_json_path ~152 tokens
  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.
• extract_links ~142 tokens
  Extract all URLs, email addresses, and domain names from text. Returns categorized and deduplicated results. Useful for content auditing, link checking, and web scraping validation.
• extract_todos ~170 tokens
  Extract TODO, FIXME, HACK, BUG, NOTE, OPTIMIZE, and custom tags from any source code or text. Returns line numbers, tag types, and message text. Essential for technical debt auditing.
• fetch_confluence_page ~299 tokens
  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.
• fetch_jira_issue ~321 tokens
  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.
• fetch_veille_feed ~217 tokens
  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 "n
• few_shot_formatter ~222 tokens
  Format few-shot examples for LLM prompts. Converts example pairs into formatted blocks. Supports chat format (User/Assistant), XML tags, Markdown, or plain text.
• find_tool ~283 tokens
  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.
• fix_gherkin ~371 tokens
  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 f
• flatten_json ~168 tokens
  Flatten a nested JSON object to single-level dot-notation keys (e.g. {"a":{"b":1}} → {"a.b":1}), or unflatten dot-notation keys back to a nested object. Supports custom separators.
• format_bytes ~156 tokens
  Convert raw byte counts to human-readable sizes in SI (KB=1000) or IEC (KiB=1024) units, or parse size strings back to bytes. Covers B, KB/KiB, MB/MiB, GB/GiB, TB/TiB, PB/PiB.
• format_json ~217 tokens
  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
• format_table ~188 tokens
  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.
• function_call_validate ~231 tokens
  Validate an LLM function call / tool_use output: check that function name is in allowed list, arguments match expected schema, no extra/missing args. For OpenAI function calling & MCP tool_use testing.
• generate_ci_workflow ~868 tokens
  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-
• generate_curl ~263 tokens
  Generate a curl command from request parameters. Supports GET/POST/PUT/DELETE, custom headers, JSON body, and form data. Useful for documentation, sharing, and debugging API calls.
• generate_eval_yaml ~328 tokens
  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, c
• generate_hmac ~187 tokens
  Compute an HMAC signature for a message using a secret key. Supports SHA-256 (default), SHA-512, SHA-1, and MD5. Used for API request signing, webhook verification (GitHub, Stripe, Twilio), and JWT validation.
• generate_html_report ~140 tokens
  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.
• generate_json_ld ~238 tokens
  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.
• generate_password ~188 tokens
  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.
• generate_slug ~144 tokens
  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.
• generate_test_cases ~359 tokens
  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
• generate_uuid ~121 tokens
  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.
• get_testing_guidelines ~403 tokens
  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-testin
• guardrail_test ~250 tokens
  Test an LLM response against a set of guardrail rules: must-include, must-not-include, max length, required format, language, forbidden patterns, and custom regex. Returns pass/fail per rule.
• hallucination_check ~343 tokens
  Lexical hallucination check: verifies an LLM answer's words, numbers and polarity against the provided source/context. Fast, deterministic, no API key needed. Each answer sentence is aligned to its best-matching source sentence, so a number only counts as support when it sits on the SAME statement (
• hash_text ~147 tokens
  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.
• html_to_markdown ~131 tokens
  Convert HTML to clean Markdown. Strips scripts, styles, nav, ads, and comments. Converts headings, lists, links, images, code blocks. Ideal for preparing web content as LLM context.
• http_status_lookup ~185 tokens
  Look up detailed information about any HTTP status code: class, name, description, cacheability, typical causes, and handling best practices. Covers every code in the IANA HTTP Status Code Registry (1xx-5xx, including 226, 425, 451, 508, 511 and the WebDAV codes) with its defining RFC; anything outs
• identify_caller ~147 tokens
  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.
• jira_to_test_suite ~548 tokens
  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
• json_diff ~276 tokens
  Compute a deep structural diff between two JSON values. Returns added, removed, and changed keys with dot-notation paths. Like git diff but for JSON objects — perfect for API response regression testing. Arrays are not compared blindly by position: the same elements in a different order collapse to
• json_schema_generate ~215 tokens
  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 LL
• json_schema_validate ~200 tokens
  Validate a JSON value against a JSON Schema (draft-07 subset). Supports type, required, properties, items, enum, const, pattern, format (email/uri/date), minimum/maximum, minLength/maxLength, minItems/maxItems, uniqueItems, additionalProperties, anyOf, allOf, oneOf. Returns all validation errors wit
• json_to_csv ~155 tokens
  Convert a JSON array of objects to CSV format. Automatically detects columns from all object keys. Handles quoting and escaping per RFC 4180.
• json_to_yaml ~135 tokens
  Convert a JSON object to clean, human-readable YAML. Handles nested objects, arrays, multiline strings, and special characters. No external dependencies.
• latency_benchmark ~293 tokens
  Measure response time of one or more HTTP endpoints (GET/POST). Runs N iterations and returns min/max/avg/p95 latency. Useful for API and MCP server benchmarking.
• levenshtein_distance ~196 tokens
  Compute the Levenshtein (edit) distance and normalized similarity ratio between two strings. Supports batch comparison. Useful for fuzzy string matching, deduplication, and test result comparison.
• lint_commit_message ~153 tokens
  Validate a git commit message against the Conventional Commits spec (feat, fix, docs, style, refactor, test, chore, ci, perf, build). Returns compliance score, breaking change detection, and actionable suggestions.
• list_llm_models ~172 tokens
  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. Use this to discover which models are available before calling an LLM API, or to compare providers.
• list_local_tests ~106 tokens
  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.
• llm_fit_finder ~351 tokens
  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.
• llm_format_check ~194 tokens
  Validate that an LLM output matches an expected format: JSON, Markdown, code block, bullet list, numbered list, table, YAML, XML, or custom regex. Essential for structured output testing.
• llm_generate ~351 tokens
  Generate text using open-source LLM models hosted on Groq (ultra-fast) or HuggingFace Inference (serverless). No API key required — the server provides its own keys. Supported models: Qwen3 32B, Gemma 4 27B, Gemma 3 27B, Llama 3.3 70B, Llama 4 Scout, DeepSeek R1, Mistral Small 24B, and more. Use lis
• llm_json_schema_check ~153 tokens
  Validate that an LLM JSON output matches a JSON Schema definition. Tests required fields, types, enums, nested objects, and arrays. Critical for function-calling and structured output testing.
• llm_output_validator ~397 tokens
  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.
• lorem_ipsum ~172 tokens
  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).
• mcp_schema_lint ~130 tokens
  Lint an MCP tool definition for best practices: naming conventions, description quality, schema completeness, required fields consistency, description length. Returns actionable warnings.
• mcp_server_evaluate ~426 tokens
  Run a compliance evaluation against a live MCP server over Streamable HTTP, the way a client meets it. Checks: initialize (protocol version, serverInfo, Mcp-Session-Id carried afterwards), notifications/initialized (202), tools/list with pagination, the spec's tool-definition rules, ping, an unknown
• mcp_server_health_check ~396 tokens
  Check an MCP server's tool definitions without contacting it — for a server not deployed yet, a stdio server, or a CI gate. Paste the answer to tools/list (raw JSON-RPC or its result), a bare array of tools, one tool definition, or a manifest carrying name/description; input_shape says which one was
• merge_json ~212 tokens
  Deep merge two JSON objects. Supports three array strategies: replace (default), concat, or unique (dedup concat). Nested objects are recursively merged — override takes precedence for primitives.
• metamorphic_check ~1210 tokens
  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 tha
• minify_js ~118 tokens
  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.
• mock_from_schema ~209 tokens
  Generate realistic mock data from a JSON Schema. Supports all common types (string, number, integer, boolean, array, object, null), format hints (email, date, date-time, uri, uuid), enum, const, and nested schemas. Perfect for testing MCP tools with realistic data.
• model_info ~136 tokens
  Get detailed specs for an AI model: context window, pricing per 1K tokens, knowledge cutoff, provider, multimodal support, reasoning capabilities, and feature list. Covers 30+ models from OpenAI, Anthropic, Google, DeepSeek, Meta, Mistral, Cohere, xAI.
• multimodal_eval_guide ~678 tokens
  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.
• needle_haystack_generate ~262 tokens
  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.
• normalize_vector ~143 tokens
  L2-normalize a float vector (produce a unit vector with norm=1). Required by many vector DBs (Pinecone, Qdrant cosine). Supports batch normalization of up to 1000 vectors.
• normalize_whitespace ~279 tokens
  Normalize whitespace: trim trailing spaces, collapse blank lines, normalize line endings (LF/CRLF), convert tabs to spaces. Useful for cleaning code, configs, and text before processing.
• number_base_convert ~152 tokens
  Convert numbers between bases: decimal, binary, octal, hexadecimal, or any base 2–36. Auto-detects 0x, 0b, 0o prefixes.
• openapi_validate ~205 tokens
  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 v
• optimize_prompt_tokens ~197 tokens
  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.
• parse_csv ~228 tokens
  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
• parse_http_headers ~149 tokens
  Parse a raw HTTP headers block into a structured JSON object. Detects multi-value headers, masks Authorization values, and optionally audits for missing security headers (HSTS, CSP, X-Frame-Options, etc.).
• post_jira_comment ~232 tokens
  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.
• pr_gatekeeper ~247 tokens
  Compound quality gate for pull requests. Runs three sequential checks: (1) secret detection — scans diff for API keys, tokens, passwords matching 16 regex patterns; (2) bug analysis — heuristic scan for eval(), innerHTML, empty catch, console.log, TODO/FIXME; (3) commit message linting against Conve
• prompt_injection_scan ~329 tokens
  Scan user input or prompts for common prompt injection patterns. Detects system prompt overrides, jailbreak attempts, role manipulation, encoding tricks, delimiter attacks (chat-template tags `<|im_start|>`/`[INST]`/`<<SYS>>` AND fake role headers imitating markdown or chat separators: "### System:"
• prompt_template_fill ~182 tokens
  Fill a prompt template with variables. Supports {{variable}} syntax and {{#if key}}...{{/if}} conditional blocks. Returns the filled prompt and lists unfilled variables.
• prompt_test_suite ~359 tokens
  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.
• rag_relevance_rank ~147 tokens
  Rank an array of text chunks by relevance to a query using TF-IDF scoring. Simulates retrieval ranking for RAG testing without needing embeddings or an API.
• rate_tool ~352 tokens
  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 suc
• redact_pii ~197 tokens
  Automatically detect and redact Personally Identifiable Information (PII) from text. Replaces emails, phone numbers, SSNs, credit cards, IP addresses, and JWT tokens with [REDACTED_TYPE] placeholders. Safe to use before logging or sending to an LLM.
• regex_test ~260 tokens
  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 thre
• rerank_evaluate ~559 tokens
  Evaluate RAG retrieval quality: rank passages against a query and compute Precision@k / Recall@k plus a PASS/FAIL CI verdict from ground-truth relevance labels. Three modes, all keyless except the last. (1) BYO scores — give each passage the `score` your own reranker produced (Cohere, Jina, a self-h
• response_quality_score ~305 tokens
  Score an LLM response against the criteria you pass: coverage of expected_keywords and compliance with max_length. Returns a 0-100 score over the criteria actually measured — or total_score null with the reason when none is given, because relevance and correctness cannot be read off word overlap (a
• run_eval_contract ~451 tokens
  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.
• run_pr_gate_pipeline ~273 tokens
  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 sever
• run_semantic_tests ~477 tokens
  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: pip
• run_vlm_test_suite ~620 tokens
  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 stat
• run_vlm_test_suite_batch ~620 tokens
  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,
• sandbox_scenario ~418 tokens
  Get a ready-made selector-drift test case with a known-correct answer, for testing this MCP server or an agent workflow end to end. Each scenario is a real DOM capture of a deliberately breakable app, taken before and after one specific UI change (a renamed label, two swapped buttons, a duplicated l
• score_geo_signals ~136 tokens
  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.).
• search_jira_issues ~298 tokens
  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.
• secret_scan ~302 tokens
  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
• security_headers_check ~342 tokens
  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, Cro
• shield_analyze ~533 tokens
  Run a comprehensive AI guardrail analysis on an LLM response. Orchestrates 7 deterministic safety checks plus an optional LLM-powered deep analysis in parallel: hallucination detection (grounding score), prompt injection scan on BOTH the response and the original prompt (reported separately as check
• similarity_score ~310 tokens
  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.
• sort_lines ~210 tokens
  Sort, deduplicate, reverse, or filter lines of text. Useful for cleaning import lists, dependencies, log files, and config entries.
• split_chunks ~145 tokens
  Split text into chunks of at most N tokens (cl100k_base: ~4 chars/token) with optional overlap. Designed for RAG ingestion pipelines.
• ssl_certificate_check ~186 tokens
  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 befo
• strip_markdown ~133 tokens
  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.
• system_prompt_builder ~272 tokens
  Build a structured system prompt from components: role, task, constraints, output format, tone, language, and examples. Generates a production-ready system prompt with token estimate.
• test_skill ~924 tokens
  Test a SKILL.md in the open Agent Skills format (https://agentskills.io/specification — read by Claude Code, Codex, GitHub Copilot, Cursor, Gemini CLI, OpenCode and others) in two stages. (1) lint: the spec's frontmatter rules checked without a model (name 1-64 chars, lowercase/digits/single hyphens
• text_stats ~188 tokens
  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
• timestamp_convert ~125 tokens
  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.
• token_budget_calculator ~290 tokens
  Plan token allocation across system prompt, user input, context/RAG chunks, and expected output. Warns if budget exceeds model context window. Supports 25+ models.
• toxicity_scan ~209 tokens
  Scan text for toxic language, hate speech, bias/stereotype framing, violence, sexual and self-harm content. Lexical + structural pattern matching (identity term + predicate), not a semantic classifier — returns per-category risk plus the named rules that fired, so every finding can be checked. Usefu
• transform_json_array ~423 tokens
  Transform a JSON array using common operations: pluck (extract specific fields), filter (by field value), sort_by (field), group_by (field), count_by (field), uniq_by (field). Useful for processing MCP tool results and LLM structured outputs.
• truncate_to_tokens Deletes data · ~188 tokens
  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.
• unescape_html ~122 tokens
  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.
• url_decode ~93 tokens
  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.
• url_encode ~130 tokens
  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.
• validate_agent_trajectory ~933 tokens
  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 (a
• validate_email ~110 tokens
  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.
• validate_mcp_response ~368 tokens
  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.
• validate_url ~86 tokens
  Parse and validate a URL. Returns decomposed components: protocol, hostname, port, path, query parameters, hash, and origin.
• vector_quantize ~152 tokens
  Simulate int8 or int4 quantization of float32 embedding vectors. Reduces storage by 4x (int8) or 8x (int4). Returns quantized values, scale factor, and precision loss (MSE). Useful for understanding vector DB compression trade-offs.
• vector_similarity ~196 tokens
  Compute similarity/distance between two float vectors: cosine similarity, dot product, Euclidean and Manhattan distance. Essential for vector DB relevance scoring, embedding evaluation, and nearest-neighbor testing.
• vector_stats ~224 tokens
  Compute statistics for a float vector or matrix of vectors: mean, std, L2 norm, min, max, sparsity, top-K indices. Useful for debugging embedding quality and analyzing vector distributions in a vector DB.
• web_security_audit ~286 tokens
  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
• webhook_endpoint_create ~163 tokens
  Create a temporary webhook endpoint that captures incoming HTTP requests for one hour. Returns the webhook id, public URL, expiration timestamp, and current request count. Use together with webhook_endpoint_requests to inspect captured payloads.
• webhook_endpoint_requests ~138 tokens
  Fetch the requests captured by a webhook created with webhook_endpoint_create. Returns the newest requests first with method, headers, query params, body payload, and timestamps.
• word_frequency ~183 tokens
  Analyze word frequency in text. Returns top N words with counts and percentages. Supports English stopword filtering. Useful for content analysis, keyword extraction, and LLM output analysis.
• xml_to_json ~177 tokens
  Convert an XML string to a JSON object. Supports attributes, nested elements, arrays, CDATA, and namespaces. Options: parse numbers, parse booleans, ignore attributes.
• yaml_to_json ~168 tokens
  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.

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