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Server Quality Checklist

67%
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  • Latest release: v2.2.0

  • Disambiguation5/5

    Only one MCP tool is exposed, so there is no possibility of selecting the wrong tool. The internal codemode methods are clearly named and described, leaving no ambiguity about what each call does.

    Naming Consistency4/5

    The single tool name 'docs' is simple and matches the documentation domain. Internal methods follow a consistent snake_case pattern (e.g., nextjs_docs, react_index), and no conflicting naming conventions appear across the tool surface.

    Tool Count2/5

    With only one MCP tool, the server feels severely undersurfaced for its broad scope covering nine documentation stacks. The design hides ~20 callable methods behind a single JS sandbox, reducing discoverability and forcing agents to write code to access basic functionality.

    Completeness5/5

    The tool provides comprehensive coverage of the documentation domain: per-stack indices, path-based doc fetchers, and a cross-stack search with optional content fetching. There are no obvious gaps in the workflow.

  • 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
    • No commit activity data available
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI is passing
  • 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": [
        "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

  • Behavior5/5

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

    No annotations are provided, so the description carries the full burden of behavioral disclosure, and it does so thoroughly. It discloses the 30s async timeout, the unbounded nature of sync infinite loops, the one-call/one-result no-streaming model, Node 18+ globals, and that console.log output is captured in a separate [logs] block. This gives the agent a complete safety and execution model.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness4/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is front-loaded with the core concept and then systematically expands into SDK details, type definitions, and examples. However, it is quite verbose, with repeated boilerplate for each of the nine documentation stacks (identical workflow comments and near-identical type definitions). The structure is clear, but trimming redundant per-stack repetition would make it more concise without losing value.

    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?

    Despite having no output schema, the description embeds output type definitions for every callable and explains return shapes concretely. It covers the complete sandbox environment, all available SDK methods, error handling via returned error fields, and even points to a deeper MCP resource for advanced patterns. This is exceptionally complete for a tool of this complexity.

    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?

    While the schema has 100% coverage for the single `code` parameter, the description adds immense semantic value beyond the schema's one-line description. It explains that the code must be an async arrow function, what globals are available, how to use the codemode SDK, and includes working examples. The example code alone supersedes the schema in teaching correct parameter usage.

    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 opens with a specific verb+resource statement: "Run JavaScript in a local Node sandbox. Write ONE async arrow function that returns a value." This clearly distinguishes the tool's role as an execution harness for the bundled documentation SDK. It goes beyond the name 'docs' by explaining the mechanism and the expected function shape.

    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 the fan-out capability: "Fan out independent calls with Promise.all — that is why this tool exists." It also gives detailed usage workflows for internal tools like docs_search, including when to use search versus direct doc fetches. The repeated workflow steps ('Call index first, then fetch docs') provide clear, actionable guidance.

    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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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.

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