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MaryamZi

rag-retrieval-mcp

by MaryamZi
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Server Quality Checklist

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

  • Latest release: v0.2.0

  • Disambiguation5/5

    With only one tool, there is no ambiguity. The tool has a clear, single purpose to retrieve content from a knowledge base.

    Naming Consistency5/5

    There is only one tool, so naming consistency is not an issue. The name 'retrieve' is a straightforward verb describing the action.

    Tool Count2/5

    A single tool for a RAG retrieval server feels insufficient. Typically, such a server would require additional tools for knowledge base management, such as adding or deleting documents.

    Completeness1/5

    The tool surface is severely incomplete. Only retrieval is supported, with no tools for managing the knowledge base (e.g., create, update, delete documents), leaving agents unable to perform basic lifecycle operations.

  • Average 2.9/5 across 1 of 1 tools scored.

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

    • No community issues in the last 6 months
    • 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 Apache 2.0.

  • This repository includes a README.md file.

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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

  • Behavior2/5

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

    With no annotations provided, the description carries full burden for behavioral disclosure. It only mentions 'return relevant content' without detailing side effects, authentication requirements, rate limits, or whether the tool is read-only. This is insufficient for a tool with no annotation support.

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

    Conciseness3/5

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

    The description is short (two sentences) and front-loaded with the main purpose. The second sentence restates the parameter name without adding value, which could be removed. It is not overly verbose but could be tighter.

    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 simple tool (one required parameter, no nested objects) and the presence of an output schema, the description provides baseline completeness. However, it lacks context about result format, pagination, or what 'relevant content' entails, leaving some ambiguity.

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

    Parameters3/5

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

    The input schema coverage is 0%, so the description must compensate. It describes the 'query' parameter as 'The search query to find relevant content,' which adds some semantic meaning beyond the schema's type string. However, it lacks details on expected format, length, or examples, which would be more helpful.

    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 verb 'search' and the resource 'knowledge base', making the tool's purpose unambiguous. However, it lacks specificity about the type of knowledge base, which could be improved.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines2/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    No guidance is provided on when to use this tool versus alternatives. Without sibling tools or usage context, the agent has no basis for deciding when this search is appropriate.

    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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