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

run_pr_gate_pipeline

Read-onlyIdempotent

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 severities, impacted risk areas), generated test cases, and a PASS / CONDITIONAL / BLOCK recommendation. Advisory: the score measures properties of the diff, not the correctness of the change — it does not read the code semantically and does not replace a reviewer or a static analyser. See notAnalysed in the response.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`context`	No	Optional PR title or description for richer analysis
`git_diff`	Yes	Unified git diff (output of `git diff HEAD` or copied from GitHub diff view)

Output Schema

TableJSON Schema

Name	Required	Description	Default
`sla`	No
`high`	No
`topBugs`	No
`critical`	No
`bugsFound`	No
`riskLevel`	No
`riskScore`	No
`disclaimer`	No
`impactAreas`	No
`inputFormat`	No
`notAnalysed`	No
`riskFactors`	No
`changedFiles`	No
`severityLevel`	No
`testCasesGenerated`	No
`mergeRecommendation`	No

TDQS

A4.5/5.0

Behavior5/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds behavioral context: the risk score is based on diff properties, not semantic correctness, and it explicitly states what the tool does not do. This goes beyond the structured 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?

The description is dense but well-structured: a one-sentence purpose, a list of outputs, and an advisory. Every sentence provides necessary information without padding.

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 complex tool, the description covers inputs, outputs, risk factors, and limitations. It mentions the notAnalysed field, indicating awareness of what's not included. The presence of an output schema means it needn't detail return structure, and it doesn't.

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% with descriptions for git_diff and context. The description repeats the git_diff input but adds no new parameter semantics beyond saying 'Takes a unified git diff'. The optional context parameter is not mentioned in the description. 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 'Review triage for a pull request' and lists specific outputs (lint findings, regression areas, risk score, test cases, recommendation). This distinguishes it from siblings like pr_gatekeeper and analyze_diff_bugs by focusing on diff-property triage.

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?

The advisory explicitly scopes the tool: it measures diff properties, not correctness, and doesn't replace a reviewer or static analyser. This gives context on when not to use it, though it doesn't name alternative tools. 'See notAnalysed in the response' further hints at its limitations.

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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