{ "voice": "One card of the book.", "agent": { "slug": "ia-qa-eval-rag-38-tools", "name": "IA-QA Eval & RAG — 38 tools", "specialty": "research", "description": "LLM & RAG evals: hallucination, eval contracts, semantic tests, tokens & model info. 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Returns matching tool names, descriptions, and optionally their inputSchemas. Queries…" }, { "name": "hallucination_check", "description": "Lexical hallucination check: verifies an LLM answer's words, numbers and polarity against the provided source/context. Fast, deterministic, no API key needed…" }, { "name": "run_eval_contract", "description": "Run an IA-EVAL test suite (.ia-eval.yaml contract): call the model per scenario; return passed (true, false, null if undecided) and a verdict (PASS, FAIL…" }, { "name": "run_semantic_tests", "description": "Semantic assertion primitive: compare actual vs expected text pairs using cosine similarity + ROUGE-L. Two modes: tfidf (default, free, no API key) or…" }, { "name": "generate_ci_workflow", "description": "Generate a ready-to-commit GitHub Actions workflow that gates a build on IA-QA: \"eval_contract\" runs a .ia-eval.yaml through ia-qa-com/eval-action@v1 (needs a…" }, { "name": "needle_haystack_generate", "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…" }, { "name": "generate_eval_yaml", "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…" }, { "name": "metamorphic_check", "description": "Reference-free stability check: does an assistant's answer stay the same when the QUESTION is transformed (case, typo, paraphrase, reorder, translation)? You…" }, { "name": "test_skill", "description": "Test a SKILL.md (Agent Skills format). (1) lint: the spec's frontmatter rules, no model — an error returns BLOCK; gate on lint.valid. (2) triggering: a model…" }, { "name": "llm_output_validator", "description": "Validate an LLM response against QA criteria: format checks (JSON, code, markdown), content rules (must-include, must-not-include), length constraints…" }, { "name": "compare_responses", "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…" }, { "name": "analyze_responses", "description": "Semantically analyze N already-produced model outputs for the SAME task (the MCP counterpart to the LLM Sandbox). Without a reference: computes consensus…" }, { "name": "llm_format_check", "description": "Validate that an LLM output matches an expected format: JSON, Markdown, code block, bullet list, numbered list, table, YAML, XML, or custom regex. Essential…" }, { "name": "consistency_check", "description": "Compare multiple LLM responses to the same prompt and detect inconsistencies using Jaccard word-overlap similarity and fact drift (number comparison). Fast…" }, { "name": "llm_json_schema_check", "description": "Validate that an LLM JSON output matches a JSON Schema definition. Tests required fields, types, enums, nested objects, and arrays. Critical for…" }, { "name": "response_quality_score", "description": "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…" }, { "name": "conversation_analyze", "description": "Analyze a multi-turn conversation for context retention, topic drift, instruction following, and repetition. Accepts messages array [{role, content}]…" }, { "name": "cot_analyzer", "description": "Analyze a Chain-of-Thought (CoT) or reasoning trace from an LLM. Detects step count, logical flow, conclusion presence, backtracking, and estimates reasoning…" }, { "name": "ab_test_report", "description": "Generate an A/B test report comparing two prompts or model configurations. Accepts arrays of scores and returns mean, median, std deviation, improvement…" }, { "name": "similarity_score", "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…" }, { "name": "run_vlm_test_suite", "description": "Test a vision model (OpenAI, Anthropic or Gemini) on one image: each test case asks a question and asserts on the answer (contains, not_contains, json_format…" }, { "name": "embedding_similarity", "description": "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…" }, { "name": "rag_relevance_rank", "description": "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." } ] }, "reach": { "type": "mcp", "door": "https://www.ia-qa.com/mcp/eval", "answers": true, "checked": "2026-11-04T06:02:49.922Z", "http": 200, "how": "MCP server — connect an MCP client to this address.", "badges": [ { "kind": "mcp", "state": "verified", "checked": "2026-11-04T06:47:37.633Z", "since": "2026-11-04T06:47:37.633Z" } ], "verified": { "kind": "mcp", "state": "verified", "checked": "2026-11-04T06:47:37.633Z", "since": "2026-11-04T06:47:37.633Z" } }, "liveness": { "answers": true, "http": 200, "checked": "2026-11-04T06:02:49.922Z", "last_answer_at": "2026-11-04T06:02:49.922Z", "last_ms": 88, "checked_at": "2026-11-04T06:02:49.922Z" } } }