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mvagnon

opencode-mcp

by mvagnon
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TypeScript
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Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.0

  • Disambiguation5/5

    With only one tool, there is no possibility of confusion or overlap. The tool's purpose is clearly defined as read-only codebase Q&A.

    Naming Consistency5/5

    The single tool name 'ask_codebase' follows a clear verb_noun pattern, which is consistent and descriptive.

    Tool Count3/5

    One tool feels thin for a general-purpose MCP server, but it is appropriately focused for a specialized read-only Q&A assistant. The count is borderline but acceptable.

    Completeness5/5

    The tool covers the entire stated domain of asking questions about a codebase, with session state, branch selection, and error handling. No obvious gaps for its intended purpose.

  • Average 4.9/5 across 1 of 1 tools scored.

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

    • No community issues in the last 6 months
    • 3 commits in the last 12 weeks
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI is failing

  • This repository is licensed under MIT License.

  • 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": [
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  ]
}

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Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

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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

  • Behavior5/5

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

    The description adds substantial behavioral context beyond the readOnlyHint and openWorldHint annotations: it explicitly states 'READ-ONLY: never writes, edits, builds, or executes anything', explains the harness's repo fetching and caching behavior, fails-fast with git errors, and details session statefulness with continue_session. No contradictions with annotations.

    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 (four sentences) and front-loaded with the core purpose. Every sentence delivers unique value: purpose, safety, fetch behavior, statefulness, and usage examples. No fluff or repetition.

    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 description fully covers the tool's behavior, safety, statefulness, fetch mechanics, and use cases. The input schema thoroughly documents all four parameters, and the description complements it without needing an output schema. It is complete for an AI agent to decide when and how to invoke the tool.

    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 enriches parameter understanding beyond the schema by explaining deterministic repo slugs, session semantics associated with continue_session, and branch behavior (e.g., re-fetch on explicit branch). This adds meaningful context rather than merely repeating schema descriptions.

    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 precisely states that the tool answers natural-language questions about a GitHub repository with answers grounded in real code. The verb 'Ask' and resource 'codebase' are specific and there are no sibling tools requiring differentiation.

    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?

    It explicitly lists use cases: 'architecture questions, where a feature lives, request flow, conventions, design rationale, etc.' This gives clear context on when to use the tool, even though no alternatives exist to contrast.

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

GitHub Badge

Glama performs regular codebase and documentation scans to:

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

Our badge communicates server capabilities, safety, and installation instructions.

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