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qmed-asia

Qmed-AskCPG

Official
by qmed-asia
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Server Quality Checklist

50%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.1.5

  • Disambiguation5/5

    With only one tool, there is no risk of confusion between tools. The agent can uniquely identify the tool's purpose.

    Naming Consistency5/5

    The single tool name 'get_cpg_context' follows a clear verb_noun pattern, consistent with common MCP naming conventions.

    Tool Count3/5

    A single tool for querying clinical guidelines is minimalist but functional. The server's scope is narrow, yet a few more tools (e.g., list_guidelines) could improve coverage.

    Completeness3/5

    The server provides only retrieval of context from medical books. Missing operations like listing available guidelines or retrieving full sections, but the tool may suffice for simple Q&A tasks.

  • Average 2.6/5 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
    • Last stable release on
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI status not available

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  • 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 must disclose behavioral traits. It fails to mention what 'context' means (e.g., snippets, full paragraphs), how results are ordered or filtered, or any authentication or rate limit requirements. The tool appears to be a retrieval function but offers no details on behavior.

    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 but includes an 'Args' section, providing some structure. However, the main sentence is slightly redundant ('based on user query' and 'user query'). It could be tightened without losing meaning.

    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?

    Given the lack of output schema and low schema coverage, the description should be more complete. It does not specify the return format, error cases, or what constitutes a valid query. For a retrieval tool, this is insufficient.

    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?

    The schema has 0% description coverage; the input_queries parameter has only a title. The description adds 'user query to get the context from the medical books,' which merely restates the parameter's role. No format constraints, length limits, or query structure hints are provided.

    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: getting context from medical clinical practice guideline books based on a user query. The verb 'Get' and resource 'context' are specific, and the domain is clear. However, no sibling tools are provided for comparison, so the score is not a 5.

    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?

    The description lacks any guidance on when to use this tool vs alternatives, prerequisites, or limitations. It simply states the action without contextual boundaries. No 'when not to use' or alternative tool suggestions are given.

    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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  • 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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