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

similarity_score

Read-onlyIdempotent

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.

Input Schema

TableJSON Schema

Name	Required	Description
`batch`	No	Batch mode: array of {reference, hypothesis} pairs.
`metrics`	No	Metrics to compute (default: all). Options: "cosine_bow", "cosine_tfidf", "jaccard", "rouge1", "rouge2", "rougeL", "bleu"
`reference`	No	Reference / expected text (ground truth)
`threshold`	No	Optional pass/fail threshold (0-1). Applies to ROUGE-L F1 score.
`hypothesis`	No	Hypothesis / actual text (LLM output)

Output Schema

TableJSON Schema

Name	Required	Description	Default
`f1`	No
`mode`	No
`count`	No
`recall`	No
`results`	No
`precision`	No

TDQS

A4.2/5.0

Behavior4/5

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

Annotations already declare readOnly/idempotent/non-destructive. The description adds that no API key is required and that batch mode is supported, giving useful operational context beyond the annotations. There is no contradiction.

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?

Two concise sentences front-load the purpose and metric list, then add use cases and batch support. No filler or redundancy.

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

Completeness4/5

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

The description covers purpose, use cases, metrics, auth requirements, and batch mode. The threshold parameter's behavior is in the schema, and the output schema exists, so return values are covered. It is complete for a read-only scoring tool.

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?

Schema coverage is 100%, so the schema already documents all five parameters. The description mentions the metric names and batch mode, but doesn't add 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 clearly states 'Compute text similarity between reference and hypothesis' with a specific verb and resource, then enumerates six distinct metrics. This distinguishes it from sibling tools like embedding_similarity or levenshtein_distance by specifying the exact metric set and batch capability.

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

Usage Guidelines4/5

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

It provides explicit use cases: 'Ideal for LLM eval (expected vs actual), RAG quality checks, and NLG benchmarking.' It does not explicitly name alternatives or exclusion criteria, but the context is clear enough for an agent to know when to invoke this tool over similar ones.

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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