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wjixiang

PubMed MCP Server

by wjixiang

Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.
  • Latest release: v1.0.0

  • Disambiguation5/5

    The two tools have completely distinct purposes: one for searching by keyword and the other for fetching full details by PMID. There is no overlap or ambiguity.

    Naming Consistency5/5

    Both tools use the consistent 'pubmed_' prefix followed by descriptive action/noun pairs (pubmed_search, pubmed_article_detail), maintaining a clear and predictable pattern.

    Tool Count4/5

    With only 2 tools, the server covers the essential PubMed operations (search and detail retrieval). While more advanced features could be added, this minimal set is well-scoped for basic article lookup.

    Completeness4/5

    The server covers the core search and detail retrieval workflow for PubMed articles. It lacks some advanced features like author-specific queries or citation networks, but it meets the primary need of searching and viewing articles.

  • Average 4.4/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 1 commit 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.

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    Tip: use the "Try in Browser" feature on the server page to seed initial usage.

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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

  • Behavior4/5

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

    Annotations already declare the tool as read-only and open-world, and the description adds useful context about the specific fields returned (title, authors, abstract, etc.), which goes beyond the annotation.

    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?

    Two sentences with the purpose front-loaded. The second sentence lists return fields; while somewhat lengthy, it is still concise and informative.

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

    Completeness5/5

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

    Given the tool's simplicity (one parameter, no nested objects) and the comprehensive list of return fields in the description, the description fully covers the needed information.

    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?

    Schema coverage is 100%, and the description does not add parameter details beyond what the schema provides (e.g., 'by its PMID' mirrors the schema description). Baseline of 3 is appropriate.

    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 uses a specific verb ('Fetch') and resource ('full record of a PubMed article'), clearly distinguishing it from the sibling tool pubmed_search, which is for searching.

    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 implies when to use (fetching details for a known PMID) and distinguishes from pubmed_search, but does not explicitly state when not to use or mention alternatives.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

  • Behavior4/5

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

    Annotations already indicate readOnlyHint=true and openWorldHint=true. Description adds that it returns a list with specific fields and supports filtering/sorting/pagination, which goes beyond annotations without contradiction.

    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?

    Two concise sentences, front-loaded with main purpose and return value, followed by supported features. No unnecessary words.

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

    Completeness5/5

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

    Given it's a search tool without output schema, description sufficiently covers return format and all capabilities. Parameter details are fully provided in schema. Sibling tool exists for detail retrieval, making this complete.

    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?

    Schema coverage is 100% with descriptions for all 4 parameters. Description adds context like date format 'YYYY:YYYY' and examples of publication types ('clinical trial', 'review'), enhancing understanding beyond schema.

    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?

    Clearly states it searches PubMed for articles by keyword and lists returned fields (PMID, title, authors, journal citation, snippet). Distinguishes from sibling tool pubmed_article_detail which likely gets article details.

    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?

    Describes supported filters (date ranges, publication types), sorting, and pagination. Implicitly contrasts with sibling by focusing on search vs. detail retrieval, but does not explicitly state when not to use.

    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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