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Agent Aggregator

Agent Aggregator

by rnd-pro
OverviewSchemaSourceRelated ServersDiscussionsAdmin
JavaScript
Remote

ai-tools__review_code

Review code to identify quality issues and enforce best practices, with optional focus areas for targeted analysis and actionable improvements.

Instructions

[AI Code Analysis] Reviews the given code for quality and best practices

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "code": "<code>"
    },
    "name": "ai-tools__review_code"
  }
}

Input Schema

NameRequiredDescriptionDefault
codeYesCode to review
focus_areasNoAreas to focus on

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.3

TDQS

A3.7/5.0
Behavior3/5

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

With no annotations, the description must carry the behavioral burden, and 'Reviews' does signal a non-mutating analysis. However, it does not explicitly state that code is not modified, nor does it describe the review output or behavioral constraints like authentication or rate limits.

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 a single compact sentence with the action and purpose front-loaded. The '[AI Code Analysis]' prefix and result clause are useful context with no redundant filler.

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

Completeness3/5

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

The tool is simple, with two well-schemaed parameters, so the description is close to sufficient. Yet with no output schema and no annotations, it leaves the exact response shape and the meaning of the optional focus_areas value to inference.

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 already documents both parameters with 100% coverage, so the description does not need to repeat them. It adds no additional meaning about focus_areas or accepted code formats, matching the baseline for high schema coverage.

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 names a specific verb ('Reviews'), a resource ('the given code'), and a clear purpose ('quality and best practices'). This separates it from siblings such as explain_code, fix_code, and analyze_project, which address different actions or scopes.

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

Usage Guidelines3/5

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

The description implies use when code-quality and best-practice feedback is needed, but it does not explicitly state when to choose review_code over explain_code, fix_code, or test_code. No exclusions or alternative-routing conditions are provided.

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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