Fetch a paper's full body text as Markdown (methods, results, protocols, inline tables) — use for deep questions the abstract can't answer. Accepts an arXiv ID (2401.12345), a PMC ID (PMC5339222), or a bioRxiv/medRxiv DOI (10.1101/…).
| 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 already declare readOnlyHint=true and openWorldHint=true. Description adds that output is Markdown with specific sections (methods, results, etc.), which adds useful context beyond 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 purpose, followed by parameter details. Every word earns its place; no redundancy.
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 the simplicity (1 param, no output schema), the description covers purpose, usage context, accepted identifier types, and output format completely. No gaps.
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% with a single parameter described. The description adds examples (e.g., '2401.12345', 'PMC5339222', '10.1101/…'), clarifying acceptable formats and the bioRxiv/medRxiv DOI form.
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 states 'Fetch a paper's full body text as Markdown', specifying the verb (Fetch) and resource (paper's full body text). It distinguishes from siblings like get_paper (likely metadata) and get_figures.
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?
Explicitly says 'use for deep questions the abstract can't answer', providing clear context. Does not explicitly exclude other scenarios, but the guidance is strong.
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.
