Resolve a gene/protein name (e.g. 'TP53', 'CD44') or UniProt accession to its 3D structure — returns the UniProt accession + AlphaFold model URL (and PDB when available). Great for a gene named in a paper's entities.
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | Gene/protein symbol (TP53, CD44) or UniProt accession (P04637). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnlyHint and openWorldHint. The description adds useful behavioral details (returns AlphaFold URL, PDB when available) without contradicting 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 covering action, input, output, and usage hint. No redundancy, perfectly front-loaded.
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 no output schema, the description adequately explains return values. It could mention handling of invalid inputs, but for a simple lookup tool it is sufficient.
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 coverage is 100%, so baseline is 3. The description adds value by providing concrete examples (TP53, CD44) and specifying the return structure, aiding parameter understanding.
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 explicitly states the tool resolves a gene/protein name to its 3D structure and lists return values (UniProt accession, AlphaFold URL, PDB). This distinguishes it from siblings like get_entities and search_papers.
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 mentions it's 'Great for a gene named in a paper's entities', which implies usage context but does not provide explicit when-not or alternatives among siblings.
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.
