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Server Quality Checklist

33%
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    This process allows Glama to run security checks on your server and enables users to deploy it.

    Read the detailed guide

  • Disambiguation5/5

    Each tool has a clearly distinct purpose: searching for reviews vs. general search, retrieving abstracts, formatting citations, and generating summaries. No overlap exists.

    Naming Consistency5/5

    All tool names follow a consistent verb_noun pattern with snake_case, using verbs like find, get, search, and summarize. The naming is predictable.

    Tool Count5/5

    Five tools is well-scoped for a PubMed search server, covering essential operations without being too few or too many.

    Completeness4/5

    The tool set covers searching, abstract retrieval, citation formatting, and summarization. It lacks full-text retrieval but abstracts suffice for most use cases.

  • Average 3.3/5 across 5 of 5 tools scored. Lowest: 2.7/5.

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

    • 0 of 1 community issues answered or closed 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.

  • No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.

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    {
      "$schema": "https://glama.ai/mcp/schemas/server.json",
      "maintainers": [
        "your-github-username"
      ]
    }

    Then . Browse examples.

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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?

    No annotations provided, and the description only states the tool searches and returns results. It lacks important behavioral details such as rate limits, data freshness, or whether the search is real-time.

    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?

    The description is concise with two sentences, front-loading the action. It wastes no words, though it could integrate more parameter hints without adding length.

    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 tool's simplicity (2 params, output schema exists), the description is incomplete. It fails to explain query format, default behavior, or any limits beyond max_results. The output schema is not referenced.

    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 description coverage is 0%. The description adds minimal context by mentioning search by 'keyword, MeSH term, or author', but does not specify query syntax or how max_results affects pagination.

    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 searches PubMed and returns PMIDs and titles, distinguishing it from siblings like find_reviews or get_abstract. However, it could be more specific about the types of queries (e.g., free text vs MeSH).

    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 on when to use this tool versus alternatives like find_reviews or get_citations. The description does not mention any prerequisites or exclusions.

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

  • Behavior3/5

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

    No annotations are provided, so the description carries the full burden. It adds the constraint 'only' to indicate filtering to reviews, which is useful. However, it does not disclose other behavioral traits like rate limits, pagination, or output format.

    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?

    The description is a single sentence, efficient and front-loaded with the tool's purpose. However, it could be slightly more structured by including parameter hints.

    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 existence of an output schema, the description need not explain return values. However, it lacks parameter semantics and usage guidelines. It is adequate for a simple tool but has clear gaps.

    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 description coverage is 0%, and the description does not explain any parameters (query, max_results). The agent must infer their meaning from the schema alone, which is insufficient.

    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 'Search PubMed for systematic reviews and meta-analyses only', which is a specific verb+resource. This distinguishes it from sibling tools like search_pubmed (general search) and get_abstract (fetching abstracts).

    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 (e.g., search_pubmed). The description does not mention context, prerequisites, or exclusions.

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

  • 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. It only states 'Retrieve full abstract', but does not disclose any behavioral traits such as error handling, rate limits, or whether the operation is read-only. Minimal transparency.

    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 with no wasted words. It efficiently conveys the tool's core function.

    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 tool's simplicity (one required parameter, no nested objects, and an output schema exists), the description is adequate but incomplete. It does not specify what the output contains (e.g., abstract text) or handle edge cases.

    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 description coverage is 0%, and the description only mentions 'given PubMed ID (PMID)' for the pmid parameter. It adds little beyond the parameter name, which is already descriptive. For a single parameter, it does not compensate for the lack of schema documentation.

    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 verb 'Retrieve' and the resource 'full abstract for a given PubMed ID (PMID)'. It distinguishes itself from sibling tools like search_pubmed or get_citations by specifying it retrieves the abstract for a single PMID.

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

    Usage Guidelines3/5

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

    The description implies use when you have a PMID and want the abstract, but it does not explicitly state when to use this tool versus alternatives like search_pubmed or find_reviews. No usage context or exclusions are provided.

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

  • Behavior2/5

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

    No annotations provided. Description only mentions a safe read operation but lacks details on error handling, input validation, or rate limits.

    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?

    Single sentence, no redundancy. Front-loaded with the essential action and examples.

    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?

    Adequate for a simple 2-parameter tool with an output schema, but lacks error handling context and usage differentiation from siblings.

    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?

    Description adds meaning to the style parameter by listing examples (AMA, APA, Vancouver), but pmid parameter is not explained beyond its name. Schema coverage is 0%, so description partially compensates.

    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 returns a formatted citation for a PMID, listing specific styles. Distinct from sibling tools like get_abstract or search_pubmed.

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

    Usage Guidelines3/5

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

    Implied usage for citation formatting, but no explicit guidance on when to use versus alternatives like get_abstract for abstracts.

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

  • Behavior2/5

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

    No annotations are provided, so the description bears full responsibility for behavioral disclosure. It only states the basic function without mentioning any side effects, access requirements, or limitations (e.g., rate limits, caching). This is a significant gap for a tool that likely performs processing.

    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 sentence that efficiently conveys the tool's purpose without extraneous words. It is front-loaded with the core action and subject.

    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?

    For a simple tool with one parameter, the description provides the essential information. However, since an output schema exists (not shown), mentioning the output structure (e.g., PICO elements) would improve completeness. As is, it is adequate but not fully detailed.

    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 single parameter 'pmid' is self-explanatory, and the schema coverage is 0% but the description adds no extra meaning. A higher score would require clarifying what constitutes a valid PMID or how to find it, which the description omits.

    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 action ('Generate a plain-language PICO summary'), the subject ('PubMed abstract'), and the format ('plain-language PICO'). It distinguishes the tool from siblings (e.g., get_abstract, search_pubmed) by specifying the summary generation purpose.

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

    Usage Guidelines3/5

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

    The description implies usage (when a plain-language summary of a PubMed abstract is needed) but provides no explicit guidance on when to use this tool versus siblings like get_abstract or find_reviews. No when-not-to-use or alternative 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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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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