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smaniches

Semantic Scholar MCP Server

semantic_scholar_recommendations

Read-onlyIdempotent

Find similar academic papers by providing a seed paper ID, with options to limit results and choose candidate pool.

Instructions

Get paper recommendations based on a seed paper.

Provide one paper you already know (e.g. paper_id='ARXIV:1706.03762') and
receive up to `limit` similar papers. from_pool picks the candidate pool:
'recent' (default, recently published papers from all fields) or 'all-cs'
(computer-science papers of any age). When steering with several positive
or negative examples, use semantic_scholar_multi_recommend instead. An
unknown seed 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.

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?

Discloses throttling (1 req/s, 10 with API key), automatic retry on 429/502/503, unknown seed ID error, and default Markdown output. Annotations already mark read-only, idempotent, open-world; description adds valuable behavioral context 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.

Conciseness5/5

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

Concise paragraph with clear front-loading and well-organized details. No extraneous content; every sentence adds value.

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 input, output format, error cases, throttling, and alternatives. With output schema present, return values are not needed in description.

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?

The description adds meaning beyond the schema by providing concrete examples (e.g., paper_id format) and explaining from_pool options. Schema descriptions already cover parameters, so the description is complementary and helpful.

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 it gets paper recommendations based on a seed paper and differentiates from the sibling tool semantic_scholar_multi_recommend for multi-example steering.

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?

Explicitly says when to use (single seed paper) and when not to (use multi_recommend for multiple examples). Also provides throttling and error handling details.

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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