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

analyze_responses

Read-onlyIdempotent

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.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`reference`	No	Optional ground-truth answer. If set, each output is also ranked by closeness to it and the closest one is named.
`responses`	Yes	The outputs to analyze (same task, N models/prompts/versions). Each item is a plain string or { "label": "GPT-4o", "text": "..." }. At least 2 required.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`count`	No
`summary`	No
`consensus`	No
`reference_ranking`	No

TDQS

A4.7/5.0

Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnly, idempotent, non-destructive. The description adds valuable behavioral traits beyond this: 'Deterministic, no LLM, no key — gate-able in CI.' It also explains the algorithmic behavior (consensus, pairwise cosine, ROUGE-L composite, closest/outlier). No contradiction with annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Five sentences, front-loaded with the core purpose. Each sentence earns its place: no-reference behavior, reference behavior, determinism/no-key/CI guarantee, input requirement, and sibling tool alternative. No redundancy or fluff.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a semantically complex tool, the description covers use cases, input constraints, behavioral guarantees, and alternatives. An output schema exists, so return-value details are already structured. The description is fully complete without being verbose.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema covers 100% of parameters and already describes the reference behavior ('If set, each output is also ranked by closeness to it...'). The description reinforces output semantics ('same task', '2+', 'reference') but adds no significant syntax or format details beyond what the schema provides. Baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description begins with a specific verb and resource: 'Semantically analyze N already-produced model outputs for the SAME task.' It clearly distinguishes this tool from the sibling compare_responses by explicitly recommending that alternative for a different use case. The scope and function are unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides explicit usage context: 'the MCP counterpart to the LLM Sandbox' and 'You bring the outputs (2+)'. It also names an alternative with a precise exclusion: 'For a 2-way head-to-head with structural JSON diff use compare_responses instead.' This fully guides when to use the tool versus alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A3.6/5.0

Disambiguation2/5

Multiple tools overlap significantly: compare_models/llm_fit_finder/model_info/list_llm_models all compare models; similarity_score/embedding_similarity/run_semantic_tests all measure text similarity; detect_secrets/secret_scan/analyze_diff_bugs/pr_gatekeeper all scan for secrets. Descriptions attempt to differentiate, but the boundaries between many tools are unclear, making selection error-prone.

Naming Consistency4/5

The vast majority of tools follow a snake_case verb_noun pattern (validate_email, generate_uuid, parse_csv), making the set mostly predictable. A few notable deviations exist (pr_gatekeeper, llm_fit_finder, cot_analyzer, jira_to_test_suite, needle_haystack_generate) but they are the exception rather than the rule.

Tool Count1/5

With 149 tools, this set is far beyond the 50+ threshold for an extreme mismatch. Even as a general-purpose QA & Dev toolkit, the sheer number overwhelms and exceeds any reasonable scope, making discovery and selection impractical.

Completeness4/5

The toolkit covers an impressively broad range: text processing, LLM evaluation, security auditing, web checks, MCP validation, Jira/Confluence integration, and more. Minor gaps exist, such as missing delete/update for webhooks and Confluence pages, and no create/update for Jira issues, but these are workable around.

Resources

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