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alejandro-ao

MCP Server Example

by alejandro-ao
OverviewSchemaRelated ServersScoreDiscussions
Python
Hybrid

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 ambiguity or overlap between tools. The tool has a single, clear purpose of searching documentation for specific libraries.

    Naming Consistency5/5

    The single tool name 'get_docs' follows a clear verb_noun pattern. Since there is only one tool, consistency is inherently perfect with no deviations to evaluate.

    Tool Count2/5

    A single tool is too few for most server purposes, as it severely limits functionality and scope. This feels thin and incomplete for a documentation search server, which might benefit from additional tools like browsing documentation structure or getting library lists.

    Completeness2/5

    The tool surface is severely incomplete for a documentation search domain. It only supports searching text, with no tools for browsing, listing available libraries, or accessing documentation metadata, creating significant gaps that will hinder agent workflows.

  • Average 3.3/5 across 1 of 1 tools scored.

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

    • No community issues in the last 6 months
    • 0 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

  • 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.

  • Add a glama.json file to provide metadata about your server.

  • If you are the author, simply .

    If the server belongs to an organization, first add glama.json to the root of your repository:

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

    Then . Browse examples.

  • Add related servers to improve discoverability.

How to sync the server with GitHub?

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

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

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

  • Behavior2/5

    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. It mentions 'latest docs' and 'search,' but fails to describe critical behaviors such as authentication needs, rate limits, pagination, error handling, or what 'latest' means (e.g., versioning or update frequency). This leaves significant gaps for a search tool.

    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 appropriately sized and front-loaded, with the core purpose stated first, followed by supported libraries and parameter details in a structured 'Args' and 'Returns' format. It avoids redundancy, but the 'Returns' section is vague ('Text from the docs'), slightly reducing efficiency.

    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?

    Given the tool's moderate complexity (2 parameters, no output schema, no annotations), the description is partially complete. It covers the purpose, parameters, and return type broadly, but lacks details on behavioral aspects like search scope, result format, or error conditions, leaving room for improvement in guiding an AI agent.

    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?

    The description adds meaningful semantics beyond the input schema, which has 0% description coverage. It explains that 'query' is for searching (e.g., 'Chroma DB') and 'library' specifies the target (e.g., 'langchain'), including examples and listing supported libraries. This compensates well for the schema's lack of descriptions, though it could detail format constraints (e.g., case sensitivity).

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose4/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description clearly states the tool's purpose: 'Search the latest docs for a given query and library.' It specifies the verb ('search'), resource ('docs'), and scope ('latest docs for a given query and library'). However, with no sibling tools mentioned, it cannot demonstrate differentiation from alternatives, preventing a perfect score.

    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 provides implied usage context by listing supported libraries ('langchain, openai, and llama-index'), which suggests when to use this tool. However, it lacks explicit guidance on when not to use it or alternatives, and does not mention prerequisites or constraints beyond the library options.

    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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