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openpharma-org

Unofficial bioRxiv MCP Server

biorxiv_info

Access bioRxiv and medRxiv preprints via a unified interface. Perform keyword searches, fetch details by DOI, list categories, and retrieve statistics.

Instructions

Unified bioRxiv/medRxiv preprint database access. 260K+ preprints. Methods: search_preprints (keyword search with date range filtering), get_preprint_details (full metadata by DOI), get_categories (list subject categories), search_published_preprints (find preprints published in journals), search_by_funder (find preprints by funding org ROR ID), get_content_statistics (submission stats), get_usage_statistics (views/downloads stats).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
doiNoPreprint DOI e.g. "10.1101/2024.01.15.575123" (for get_preprint_details)
limitNoMaximum results to return (default 30)
queryNoSearch keywords (for search_preprints — client-side filter against title/abstract)
cursorNoPagination offset (default 0)
methodYesThe bioRxiv operation to perform.
serverNoWhich server to query (default "biorxiv")
date_toNoEnd date YYYY-MM-DD (optional, defaults to today)
categoryNoSubject category filter (optional)
intervalNoTime interval for stats: "m" (monthly) or "y" (yearly)
date_fromNoStart date YYYY-MM-DD (optional)
publisherNoPublisher DOI prefix e.g. "10.1038" for Nature (for search_published_preprints)
funder_ror_idNoROR ID e.g. "021nxhr62" for NIH (for search_by_funder)
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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