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Review Checklist

review_checklist
Read-onlyIdempotent

Produce a focused pull-request review checklist for a language or stack. FREE.

Covers the things that actually break in production, with extra items per language. Typical input {"language": "python"} returns {"language": "python", "checklist": ["...", ...], "note": "..."}.

Use before a review, to decide what to look for. Not for reviewing actual code - pass code to review_diff or security_deep_dive. Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": ""}. Every call is read-only and idempotent, so after correcting the input it is always safe to retry.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
languageNoLanguage or stack to tailor for: "python", "javascript", "typescript", "go", "sql", or "general" (default). Unknown values fall back to the general checklist.general

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

TDQS

A4.9/5.0
Behavior5/5

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

The annotations already declare readOnlyHint, idempotentHint, and non-destructive nature. The description adds valuable context beyond that: it explains the error behavior ('never raises a protocol error — it returns {"error": ...}'), confirms safety for retry, and notes it is free to use ('FREE'). This fully discloses the tool's behavior.

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 remarkably concise at three short paragraphs. It front-loads the key purpose and a notable feature ('FREE.'), then efficiently covers usage, alternatives, error handling, and safety. Every sentence adds distinct value without redundancy.

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?

Given the tool's simplicity (1 optional parameter, no required fields) and the presence of both input and output schemas, the description is complete. It explains the expected input, the output structure (with a concrete example), error handling, and fallback behavior. There are no missing elements needed for an agent to use this tool correctly.

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

Parameters4/5

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

Schema coverage is 100%, so the schema already documents the 'language' parameter well. The description adds value by giving a concrete example of the input and returned structure, as well as explaining fallback behavior for unknown values. The baseline is 3; the additional context justifies a 4.

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 starts with a specific verb ('Produce') and a clear resource ('pull-request review checklist'). It explicitly distinguishes itself from sibling tools like 'review_diff' and 'security_deep_dive' by clarifying its role is to guide the reviewer on what to look for, not to analyze actual code.

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?

The description provides clear usage context: 'Use before a review, to decide what to look for.' It also explicitly states when not to use this tool ('Not for reviewing actual code') and names specific alternatives ('pass code to review_diff or security_deep_dive'). This gives the agent clear decision criteria.

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

A4.6/5.0
Disambiguation5/5

Each tool targets a distinct analysis area: AI-generated code smells, structural complexity, reviewer persona, review checklist, diff risk scanning, secret scanning, and OWASP security deep dive. Even though review_diff and security_deep_dive both touch security, one is diff-based and the other is full-file, and descriptions clarify the difference.

Naming Consistency4/5

All names use snake_case, but there is a mix of verb-first (ai_code_smell_scan, get_reviewer_persona, secret_scan) and noun-first (complexity_report, review_checklist, review_diff, security_deep_dive) patterns. This is mostly consistent but the verb usage varies (scan, get, report, checklist, dive).

Tool Count5/5

7 tools is well-scoped for a code review analysis server. Each tool serves a specific purpose without overlap, covering multiple angles (AI smells, complexity, security, secrets, diff review, checklist, persona) without being overwhelming.

Completeness4/5

The tool set covers essential static analysis tasks for code review: structural, security, secret detection, and AI-generated code detection. It also provides supporting tools (checklist, persona). A minor gap is the lack of an integrated tool that produces a consolidated review summary or comment generation from findings.

Resources