Freelance MCP Server

code_review

Review a code file with LLM analysis to get feedback on general, security, performance, or style issues. Provide the file path and optional review type to identify problems and improve code quality.

Instructions

Review code file and provide feedback using LLM analysis

Args:
    file_path: Path to the code file to review
    review_type: Type of review (general, security, performance, style)

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": {
      "file_path": "<file_path>"
    },
    "name": "code_review"
  }
}

Input Schema

Name	Required	Description	Default
`file_path`	Yes
`review_type`	No		general

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv2.1.0Sep 13, 2026

TDQS

B3.2/5.0

Behavior2/5

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

With no annotations, the description must disclose behavioral traits, but it only mentions 'using LLM analysis,' which hints at AI-driven processing but omits critical details like whether the operation is read-only, requires specific permissions, has rate limits, or returns structured feedback. The description lacks transparency about side effects and operational constraints.

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 concise and front-loaded: a single summary sentence followed by a compact Args section. Every sentence serves a purpose, and there is no redundant or filler content. The structure is easy to parse.

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?

Given the tool's simplicity (2 parameters) and the presence of an output schema, the description covers the basics of purpose and parameters. However, it falls short on usage guidelines and behavioral transparency, which are important when annotations are absent. It is adequate but incomplete for an agent to fully understand when and how to invoke it.

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?

The schema has 0% description coverage, so the description must explain parameters. It does so effectively: 'file_path: Path to the code file to review' and 'review_type: Type of review (general, security, performance, style)' gives clear meaning and even enumerates possible values. It could specify format expectations for file_path, but overall it compensates well for the schema gap.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Review code file and provide feedback using LLM analysis.' This specifies a verb (review) and resource (code file), making it easy to distinguish from general utilities. However, it does not differentiate from sibling tools like 'validate' or 'code_debug' that might also involve code analysis, so it's not a 5.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus alternatives. There is no mention of scenarios where code_review is preferred over validate or code_debug, nor any prerequisites or exclusions. The absence of any usage context warrants a low score.

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

Deploy Server