Get the biomedical entities linked to a paper: genes, proteins, chemicals, diseases, species, mutations, cell lines, and clinical-trial (NCT) IDs. Accepts an arXiv ID, PMC ID, or bioRxiv/medRxiv DOI.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | arXiv ID, PMC ID, or 10.1101/… DOI. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations (readOnlyHint, openWorldHint) already indicate safety and potential variation. The description adds transparency by listing the exact entity types returned, which is valuable beyond the annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, front-loaded with the core purpose and entity list, then supported by ID format specification. No extraneous information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given a single parameter, no output schema, and informative annotations, the description fully communicates what the tool does, what inputs are needed, and what outputs to expect.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% for the single parameter 'id', so the baseline is 3. The tool description repeats the ID formats but adds no new semantic detail beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it retrieves biomedical entities linked to a paper, enumerating specific entity types (genes, proteins, etc.), and specifies accepted ID formats. This distinguishes it from sibling tools that return other paper-related data.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description states the accepted ID types (arXiv, PMC, DOI) but does not explicitly contrast with siblings like get_paper or get_figures. However, the context is clear enough for the agent to infer appropriate use.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool has a clearly distinct purpose: citation retrieval is split into inbound and outbound, paper data is divided into metadata, full text, figures, entities, and structure, with separate search and similarity tools. No overlap.
Most tools use snake_case, but there's a mix of 'get_' prefix (get_entities, get_figures, etc.) and direct action names (citations, references, search_papers). This is minor inconsistency; overall pattern is clear.
9 tools is well within the optimal range for a scientific paper server. Each tool adds unique value without overwhelming the interface.
The server covers the full lifecycle for paper retrieval: search, metadata, full text, figures, citations (both directions), entities, structure, and similarity. No obvious gaps for its read-only purpose.
