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fastmcp-me

MCP RAG

by fastmcp-me
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Python
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Server Quality Checklist

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

  • Latest release: v0.3.16

  • Disambiguation5/5

    Only one tool exists, so there is no risk of ambiguity between tools. The tool's purpose is clear enough for an agent to identify when to use it.

    Naming Consistency5/5

    The single tool name 'ask_rag' follows a clear verb_noun convention, which is consistent even with only one tool.

    Tool Count2/5

    A RAG server typically requires tools for managing the knowledge base (add, delete, list) in addition to querying. Having only one tool is too few for the apparent scope of a RAG server.

    Completeness2/5

    The server only supports querying an existing knowledge base, with no way to ingest, update, or manage documents. This leaves significant gaps in the RAG workflow.

  • Average 3.2/5 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

  • 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.

    MCP servers without a LICENSE cannot be installed.

  • 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 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 only states a trigger condition, not the tool's actual behavior (e.g., retrieving relevant documents and generating an answer), potential side effects, or authentication requirements. This is a significant gap.

    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 a single, front-loaded sentence that efficiently communicates the primary use case without any wasted words. It is appropriately brief for a simple tool.

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

    Completeness2/5

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

    Despite having an output schema (which eliminates the need to explain return values), the description is too sparse to provide complete context. It does not explain what the tool does with the query, how it retrieves information, or any constraints. For a 1-parameter tool, this is still insufficiently complete.

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

    Parameters2/5

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

    Schema coverage is 0%, so the description must compensate for explaining the 'query' parameter. It does not describe what the query should contain, its format, or how it is used. The parameter name is self-explanatory, but no additional semantics are added beyond the schema's type and title.

    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 identifies the tool as a query mechanism for existing materials or a knowledge base, using the verb '查询' (query) and specifying the resource. It is distinct enough given the context, though it could be more explicit about the nature of the retrieval (e.g., RAG-based response).

    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?

    The description provides a clear when-to-use condition: 'when the user wants to query existing materials or needs a knowledge base.' It implies a specific context and is actionable, though it lacks explicit when-not-to-use guidance or alternative tools since no siblings are provided.

    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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