Find papers semantically similar to a given paper using SPECTER2 embeddings (trained on scientific-citation triplets). Returns nearest neighbors with TLDR. Use when the user wants 'papers like X' or 'what's adjacent to this work'.
| Name | Required | Description | Default |
|---|---|---|---|
| k | No | Number of neighbors (default 10, max 25). | |
| id | Yes | arXiv ID or OpenAlex Work ID. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
ReadOnlyHint and OpenWorldHint are already provided in annotations. The description adds context about the embedding model (SPECTER2) and output (neighbors with TLDR), enhancing transparency without contradiction.
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?
Extremely concise: two sentences covering purpose, method, and usage guidance. No redundant information, front-loaded with the key action.
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 tool's simplicity (2 parameters, no output schema), the description covers purpose, method, output, and usage. Could hint at result structure but not required. Annotations fill safety and open-world aspects.
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 clear descriptions for both parameters (id, k). The description does not add significant new meaning beyond the schema, so baseline of 3 is appropriate.
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?
Clearly states the tool finds semantically similar papers using SPECTER2 embeddings and returns neighbors with TLDR. Distinguishes from sibling tools like citations or search_papers by specifying the method and use cases ('papers like X').
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 when user wants 'papers like X' or 'what's adjacent to this work'. No exclusions mentioned, but the positive guidance is clear and sufficient for the agent to decide.
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.
