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YamadaYoukai

Code Search MCP

by YamadaYoukai
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.1.0

  • Disambiguation5/5

    The two tools have clear, non-overlapping purposes: search_code finds code locations based on text queries, while get_file_context retrieves surrounding source context for a specific hit. No ambiguity exists.

    Naming Consistency5/5

    Both tool names follow a consistent verb_noun pattern (search_code, get_file_context) using snake_case. The naming is predictable and clearly conveys the action and object.

    Tool Count4/5

    With only 2 tools, the server is minimal but focused on a narrow domain: code search and context retrieval. While some might expect additional filtering or browsing tools, the count is appropriate for a targeted utility.

    Completeness4/5

    The server covers the core workflow of searching code and retrieving file context. Minor gaps exist (e.g., no direct language-specific filter, no batch operations), but the surface is sufficient for the stated purpose.

  • Average 4.7/5 across 2 of 2 tools scored.

    See the Tool Scores section below for per-tool breakdowns.

    • No community issues in the last 6 months
    • 33 commits in the last 12 weeks
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI status not available

  • Add a LICENSE file by following GitHub's guide. Once GitHub recognizes the license, the system will automatically detect it within a few hours.

    If the license does not appear after some time, you can manually trigger a new scan using the MCP server admin interface.

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  • This repository includes a README.md file.

  • No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.

    Tip: use the "Try in Browser" feature on the server page to seed initial usage.

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    {
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}

    Then . Browse examples.

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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

  • Behavior4/5

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

    No annotations provided, so the description carries the full burden. It discloses output format (target line marked with >, start/end lines, total lines, truncation flag) and constraints (relative paths only). It does not mention error handling or side effects, but the read-only nature is implied. Transparency is good but not exhaustive.

    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 with purpose. Every sentence adds value: purpose, usage context, what not to do, output format. No redundant or verbose language. Efficiently structured.

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

    Completeness4/5

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

    Given the tool has 5 parameters with 100% schema coverage, an output schema, and no annotations, the description covers the essential aspects: when to use, parameter source, prohibitions, and output details. It could mention error behavior, but overall it is sufficiently complete for effective tool invocation.

    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 baseline is 3. The description adds value by clarifying that parameters should come from search_code hits (file_path is relative, repository is repo name, line_number is from hit's line field) and provides defaults/max for lines_after/before. This connects the schema to the usage context.

    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 the tool reads source context with line numbers around a code search hit. It uses a specific verb and resource, and distinguishes itself from sibling tool search_code by indicating it should be called after search_code returns hits.

    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 explicitly states when to use ('after search_code returns candidate hits'), provides context for what it is suitable for (confirming hit type, understanding control flow), and gives prohibitions (don't search unknown files, don't use absolute paths). This provides complete guidance.

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

  • Behavior4/5

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

    No annotations provided, so the description bears full responsibility. It discloses that the tool performs only text/regex search, no semantic analysis, and hits are not guaranteed to be definitions. It does not mention rate limits or auth, but the behavioral limitations are well-covered.

    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 efficiently structured: starts with purpose, then usage guidance, then parameter tips. Every sentence adds value, and there is no redundancy or fluff.

    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 complexity (6 parameters, output schema present, sibling tool), the description covers all necessary aspects: purpose, usage context, parameter behavior, limitations, and post-search workflow. It is fully adequate without ambiguity.

    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%, baseline 3. Description adds value by explaining default query interpretation, when to use literal=true, and provides examples for lang, path, and repo filters. This goes beyond the schema's bare definitions.

    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?

    Description clearly states the tool locates source code text and file locations in Zoekt-indexed repositories. It distinguishes from sibling tool get_file_context by specifying that this tool searches for code patterns, while the sibling handles context after hits are obtained.

    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?

    Explicitly provides when-to-use scenarios (symbol definitions, error messages, config usage, etc.), when-not-to-use (when understanding surrounding implementation, use get_file_context instead), and guidance on parameter usage (literal flag for special characters, filters for known scopes).

    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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  • Confirm that the MCP server is working as expected.
  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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