IA-QA Eval & RAG — 38 tools

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

• Connection: Runs in the cloud
• Transport: Streamable HTTP
• Category: Search & AI memory

Install

Pick the AI app you use, then copy the settings.

You need

• Runs in the cloudNothing to install; it connects over the internet

Which app do you use?

https://www.ia-qa.com/mcp/eval

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

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

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

https://www.ia-qa.com/mcp/eval

Your choice stays on this device in a cookie; no account needed.

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Tool names and descriptions exactly as the server reported them on Nov 7, 2026. The maker wrote them, not us; we show them as plain text.

• get_testing_guidelines

  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-stabili

• find_tool

  Search available MCP tools by keyword or category before calling them. Returns names, descriptions and, on request, inputSchemas. Queries are logged 90 days, screened for credentials and personal data. Categories: data, dev, encoding, eval, integrations, llm, meta, qa, rag, secur

• hallucination_check

  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

• run_eval_contract

  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, PARTIAL). Expectations are assertions that fail a scenario whatever its score; evaluators give the score. Hosted: pass inline

• run_semantic_tests

  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 ve

• generate_ci_workflow

  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 provider key as a repo secret), "cli_checks" runs deterministic tools via npx @ia-qa/cli and fails the build on their ver

• needle_haystack_generate

  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.

• generate_eval_yaml

  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 (

• metamorphic_check

  Reference-free stability check: does an assistant's answer stay the same when the QUESTION is transformed (case, typo, paraphrase, reorder, translation)? You bring the outputs; no model is called (tfidf mode is deterministic and free). Default thresholds: case .95, typo .90, para

• test_skill

  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 sees only the names + descriptions of this skill and its neighbours and picks which to load (temperature 0); trigger_accur

• llm_output_validator

  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.

• compare_responses

  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

• analyze_responses

  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 ev

• prompt_test_suite

  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.

• llm_format_check

  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.

• consistency_check

  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

• llm_json_schema_check

  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.

• response_quality_score

  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

• conversation_analyze

  Analyze a multi-turn conversation for context retention, topic drift, instruction following, and repetition. Accepts messages array [{role, content}]. Essential for chatbot QA.

• cot_analyzer

  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.

• ab_test_report

  Generate an A/B test report comparing two prompts or model configurations. Accepts arrays of scores and returns mean, median, std deviation, improvement percentage, and a Welch t-test at 95 %: `winner` names a variant only when the difference is significant — otherwise "no_signif

• similarity_score

  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.

• run_vlm_test_suite

  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, min_length, max_length, semantic_contains = TF-IDF cosine ≥ 0.4). Returns per-case PASS/FAIL, the pass rate, an overall P

• embedding_similarity

  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.

• rag_relevance_rank

  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.

• bm25_score

  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

  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.

• rerank_evaluate

  Evaluate RAG retrieval: rank passages against a query, compute Precision@k / Recall@k and a PASS/FAIL verdict from your relevance labels. Modes: (1) give every passage the `score` YOUR reranker produced — deterministic, offline, the mode to gate CI on; (2) no scores, no key — a l

• count_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.

• extract_json_from_text

  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.

• estimate_llm_cost

  Estimate the API cost in USD for a given model and token counts, at the price read daily from the provider's own pricing page (prices_as_of, price_status). Any model from list_llm_models.

• token_budget_calculator

  Plan token allocation across system prompt, user input, context/RAG chunks, and expected output. Warns if budget exceeds the model's context window (any model from list_llm_models).

• context_window_check

  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.

• model_info

  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.

• compare_models

  Put 2-5 AI models side by side: price per 1k tokens, context window, multimodal, reasoning, provider, pricing page. Facts only: names the cheapest and the largest context, never a "best model" — run your cases on each to choose.

• list_llm_models

  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 prov

• llm_generate

  Generate text with an open-source model hosted on Groq, on the server's key — no API key needed. Models: openai/gpt-oss-20b, openai/gpt-oss-120b, qwen/qwen3.8-27b (default "openai/gpt-oss-20b"). HuggingFace models are not served on the server key. A reasoning model that spends ma

• rate_tool

  Give honest usage feedback on an IA-QA MCP tool. Give 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. Maintainers read ratings, one vote per agent and tool a month: comments and p

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 7, 2026

• ! Caution

  Maker matches the repo

  The publisher (JcJamet) is not the repo owner (ia-qa-com); someone else may have listed it

• … Not checked yet

  Still maintained

  We have not read this repo from GitHub yet

• … Not checked yet

  Has a licence

  We have not read this repo from GitHub yet

• ✓ Pass

  Endpoint responds

  Responded in 911 ms (HTTP 200)

• – Not applicable

  Sign-in with OAuth

  No sign-in needed

• ✓ Pass

  No hidden instructions in tool descriptions

  Read 38 tool descriptions and found no hidden instructions to the AI

Compared with others in Search & AI memoryi

• More stars than 0% of the other 7271 servers in this category
• Published to the registry more recently than 76% of the other 7271 servers in this category
• Basic checks equal to or better than 40% of the other 7271 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Alternatives

Servers in the same category from other makers, sorted by stars.

Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi

README from the maker

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

{

{