boltwork-mcp

review_code

Review source code to identify bugs, security vulnerabilities, code quality issues, strengths, and recommended actions. Get an overall score from 1 to 10.

Instructions

Review source code and return a structured analysis covering bugs, security issues, code quality, strengths, and recommended actions. Returns an overall score 1-10. Costs 2000 sats via Lightning. Supports: Python, JavaScript, TypeScript, Go, Rust, Java, C/C++, C#, Ruby, PHP, Swift, Kotlin, Scala, Shell, SQL, Terraform, and more.

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": "review_code"
  }
}

Input Schema

Name	Required	Description
`code`	Yes	The source code to review
`language`	No	Language hint (optional, auto-detected if omitted)
`filename`	No	Filename hint for language detection (optional)

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.22May 28, 2026

TDQS

A4.1/5.0

Behavior4/5

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

The description discloses cost (2000 sats via Lightning) and lists output components. However, it lacks details on error handling, data privacy, or size limits, which are important for a code submission tool.

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?

Two concise sentences that front-load purpose and then add key behavioral info (cost, languages). Every sentence earns its place without fluff.

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 description covers purpose, output components, and cost, but with no output schema, it does not specify the exact return format (e.g., JSON structure). Lacks details on limits or error responses.

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%, and the description adds value by listing many supported languages beyond the schema's 'language hint' field. This helps agents understand tool capabilities.

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 that the tool reviews source code and returns a structured analysis covering bugs, security, quality, strengths, and recommendations, with a score. It lists many supported languages and is distinct from siblings like explain_code and review_code_url.

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 for source code review but does not explicitly guide when to choose this tool over siblings like explain_code or review_code_url. No when-not-to-use instructions are provided.

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

Deploy Server