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Server Quality Checklist

75%
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  • Latest release: v0.1.0

  • Disambiguation5/5

    With only one tool, there is no possibility of confusion between tools. The single tool is inherently distinct.

    Naming Consistency5/5

    There is only one tool, so naming consistency is not a concern. The name 'search_abstracts' follows a clear verb_noun pattern.

    Tool Count2/5

    A single tool for a comprehensive biomedical database like PubMed feels significantly under-scoped. Users would benefit from additional tools for specific operations (e.g., fetching full details, filtering by fields).

    Completeness2/5

    The server lacks tools for specialized queries, structured data retrieval, or pagination. While the single tool returns rich data, it does not cover common PubMed operations like fetching a specific article by ID or searching by author/journal.

  • Average 3.7/5 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 is passing
  • This repository is licensed under MIT License.

  • This repository includes a README.md file.

  • Tools from this server were used 2 times in the last 30 days.

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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

  • Behavior3/5

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

    No annotations are provided, so the description carries full burden for behavioral disclosure. It does not state that the operation is read-only, nor does it mention authentication needs or rate limits. However, it does describe the return format as a list of strings containing specific fields, which adds some transparency but is insufficient for a full behavioral profile.

    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 moderately concise but contains redundant phrasing (e.g., 'While it returns a free-form text in practice this is a list of strings containing'). The output fields list is helpful but could be more succinct. The structure is not front-loaded; the critical purpose statement is at the start, but the formatting could be tighter.

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

    Completeness4/5

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

    Given that an output schema exists (not shown but indicated) and the input schema is well-defined with field descriptions, the description is fairly complete for a search tool. It explains what the tool does and what the return value contains (list of strings with specific fields). It does not cover error conditions or edge cases, but for typical usage it provides sufficient context.

    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 tool description hardly adds meaning beyond the schema: it only mentions 'Args: request: SearchAbstractsRequest' without any elaboration. Schema description coverage is 0%, meaning the description does not compensate for the lack of parameter documentation in the schema text itself (though the schema object includes detailed field descriptions). The description fails to add value for parameter semantics.

    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 clearly states the tool searches abstracts on the PubMed database. It specifies the action ('search'), the resource ('abstracts'), and the database ('PubMed'). No sibling tools are provided, so differentiation is not needed, but the purpose is unambiguous.

    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?

    While no explicit when-to-use or when-not-to-use guidance is given, the description implicitly limits usage to searching PubMed abstracts. The lack of sibling tools reduces the need for alternative differentiation, but optional context on when this tool is preferred over other search methods is absent. Still, the usage scope is clear.

    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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