Skip to main content
Glama

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

pr_gatekeeper

Read-onlyIdempotent

Compound quality gate for pull requests. Runs three sequential checks: (1) secret detection — scans diff for API keys, tokens, passwords matching 16 regex patterns; (2) bug analysis — heuristic scan for eval(), innerHTML, empty catch, console.log, TODO/FIXME; (3) commit message linting against Conventional Commits spec. Returns gate verdict (PASS/WARN/BLOCK), blockers, and actionable warnings. Use before merging any code change.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
diffYesUnified git diff (output of `git diff HEAD`)
contextNoOptional: PR title or description for richer bug analysis
commit_messageYesThe commit message to lint (e.g. "feat(auth): add OAuth2 login")

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
flagsNo
scoreNo
checksNo
verdictNo

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, idempotentHint=true, and destructiveHint=false, so safety is covered. The description adds rich behavioral detail: 'three sequential checks', specific regex patterns (16), heuristic scan targets (eval(), innerHTML, etc.), Conventional Commits spec, and output types (PASS/WARN/BLOCK, blockers, warnings). This goes well beyond the 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?

Three sentences, front-loaded with 'Compound quality gate for pull requests', then a structured list of checks and output. No filler, each sentence conveys essential information. Excellent density.

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?

The tool is complex (three sequential checks) but the description covers what it does, the checks, the output format, and when to use it. Output schema exists, so return values don't need elaboration. Given the annotations and schema, this is complete.

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 has 100% coverage with clear descriptions for all three parameters (diff, context, commit_message). The description adds little beyond mapping diff to secret/bug checks and commit_message to linting—context is not explicitly mentioned. Baseline 3 is appropriate since the schema already documents parameters well.

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 the tool is a 'compound quality gate for pull requests' and enumerates three specific checks (secret detection, bug analysis, commit message linting). This distinguishes it from sibling tools like analyze_diff_bugs, detect_secrets, and lint_commit_message by being an all-in-one gate.

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?

Explicitly states 'Use before merging any code change', which gives a clear trigger for when to invoke the tool. It doesn't mention alternatives or when not to use it, but the compound nature and the presence of sibling tools imply it's the comprehensive option. Minor gap in not naming excluded alternatives.

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

Try in Browser

Glama MCP Gateway

Add one secure layer between your agents and this server.

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