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openpharma-org

Unofficial bioRxiv MCP Server

Server Quality Checklist

58%
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  • Latest release: v1.0.0

  • Disambiguation5/5

    With only one tool, there is no possibility of confusing it with other tools. The tool's description clearly enumerates its methods, so an agent can understand its purpose without ambiguity.

    Naming Consistency5/5

    The single tool name 'biorxiv_info' is clear and follows a consistent naming style. Since there are no other tools to contrast with, consistency is trivially high.

    Tool Count3/5

    A single tool is borderline for a server, but given the narrow scope of bioRxiv/medRxiv access, it can be acceptable. However, it feels thin compared to servers that expose separate tools for distinct operations.

    Completeness5/5

    The tool bundles methods covering search, metadata retrieval, category listing, publication status, funder lookup, and statistics. This provides broad coverage of preprint database operations, with no obvious dead ends.

  • Average 3.2/5 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
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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 are provided, so the description must disclose behavioral traits. It does not mention read-only nature, rate limits, authentication, data freshness, or what happens on errors. The only behavioral fact is the scale ('260K+ preprints'), but this does not convey operational behavior. The description relies on the schema for parameter details but adds no transparency about side effects or execution context.

    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 compact: a purpose statement, a scale fact, and a structured method list. It front-loads the core purpose and uses a clear enumeration. While the method list is long, each item is a concise phrase. No redundant fluff or unnecessary details, making it efficient for scanning.

    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?

    For a tool with 7 methods and 12 parameters, the description provides a high-level map of capabilities but lacks depth. It does not specify return formats, example usage, or decision trees for method selection. Given no output schema, the description only partially suffices; an agent would need to rely on the schema and method names to infer correctness.

    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 description coverage is 100%, and parameter descriptions already include method mappings (e.g., doi is 'for get_preprint_details'). The tool description lists methods but does not elaborate on parameter syntax or relationships beyond what the schema provides. Thus it adds marginal value, meeting the baseline for high schema coverage.

    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 identifies the resource (bioRxiv/medRxiv preprint database) and the action (unified access), then enumerates specific methods such as 'search_preprints' and 'get_preprint_details', which indicates the tool's scope. It does not explicitly distinguish from sibling tools (none provided), but the method list clarifies purpose. The verb 'access' is somewhat generic, but the method names add specificity.

    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 lists all available methods with brief parentheticals (e.g., 'keyword search with date range filtering' for search_preprints), implying when each might be used. However, it lacks explicit guidance on choosing between similar methods (e.g., search_preprints vs search_published_preprints) or when this tool is not appropriate. No exclusions or alternative tools are mentioned.

    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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