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smaniches

Semantic Scholar MCP Server

semantic_scholar_get_paper

Read-onlyIdempotent

Retrieve paper details—title, abstract, authors, citation counts—using S2 ID, DOI, ARXIV, or other identifiers. Optionally include lists of citing or referenced papers.

Instructions

Get paper details. Accepts: S2 ID, DOI:xxx, ARXIV:xxx, PMID:xxx, CorpusId:xxx

Returns title, abstract, authors, venue, year, citation counts, TLDR,
and open-access PDF link for one paper, e.g. paper_id='ARXIV:1706.03762'.
Set include_citations / include_references to also list citing and
referenced papers (fetched in parallel, 1-100 each). Results are cached
in memory for 5 minutes; an unknown ID raises a not-found error. Unkeyed
requests are throttled to 1 req/s (10 req/s with SEMANTIC_SCHOLAR_API_KEY)
and 429/502/503 responses retry automatically with backoff. Returns
Markdown by default, response_format='json' for raw JSON. To fetch many
papers at once use semantic_scholar_bulk_papers.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
paramsYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Disclosures go well beyond the readOnlyHint/idempotentHint annotations: caching (5 min), rate limits (1 req/s, 10 with key), automatic retry on 429/502/503, error behavior for unknown ID, and default output format.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Five sentences front-loaded with the core purpose and ID formats. Each sentence adds distinct value (citations, caching, rate limits, alternative tool). No fluff.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Covers all relevant aspects: input formats, output fields, optional features, caching, rate limiting, error handling, and alternative tool. Output schema exists, so return value details are not required.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Despite 0% schema description coverage, the description adds significant context: the parallel fetching of citations/references, the 1-100 range for limits, and example ID formats. However, it does not exhaustively detail each parameter beyond what the schema (which has descriptions) provides.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states 'Get paper details' and lists specific accessible fields (title, abstract, etc.). It also explicitly distinguishes from sibling tool 'semantic_scholar_bulk_papers' for fetching many papers.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides explicit when-to-use (get one paper) and when-not-to-use (use bulk for many papers). Also mentions rate limits, API key usage, and caching behavior, giving clear context for decision making.

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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