pow-mcp-rag-new

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search_code

Search source code and header files to find implementation details, filtering out documentation. Returns relevant code chunks based on natural language queries.

Instructions

Search only source code and header files (filters out documentation). Use when you need implementation details.

Args: query: Natural language search query project: Optional project name filter top_k: Number of results (default: 5, max: 20) headers_only: If true, search only header files (API contracts)

Returns: Relevant source code chunks

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": {
      "query": "<query>"
    },
    "name": "search_code"
  }
}

Input Schema

Name	Required	Description	Default
`query`	Yes
`top_k`	No
`project`	No
`headers_only`	No

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.1.9Aug 4, 2026

TDQS

A4.6/5.0

Behavior4/5

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

With no annotations, the description explains that it filters out documentation and includes a headers_only option to focus on API contracts. It also states the return type ('Relevant source code chunks'), adding useful behavioral context beyond just 'search'.

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 concisely structured with clear sections for Args and Returns. Every sentence adds value, no fluff, and it front-loads the key purpose and usage guidance.

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 well-covered: it has an output schema, and the description explains the return format, parameter semantics, and behavioral filtering. Given the moderate complexity and available sibling context, this description is complete for an AI agent to use correctly.

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

Parameters5/5

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

The description includes an Args section that explains each parameter (query, project, top_k, headers_only) with details like defaults and meaning, which the input schema lacks (0% description coverage). This fully compensates for the schema's barrenness.

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 'Search only source code and header files' with a specific verb and resource, and explicitly filters out documentation, distinguishing it from sibling tools like search_docs and search_specs.

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?

It provides a clear usage context: 'Use when you need implementation details.' This implies when to choose this tool over documentation searches, though it doesn't explicitly name alternatives or state when not to use it.

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