SemanticScholar_MCP

search_papers_relevance

Run relevance-ranked academic paper searches. Filter results by year, venue, citation count, publication type, and open access to find pertinent literature.

Instructions

Run one relevance-ranked /paper/search request.

Relevance search is limited by Semantic Scholar to 100 results per call and 1000 relevance-ranked results total.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`year`	No
`limit`	No
`query`	Yes
`venue`	No
`fields`	No
`offset`	No
`fields_of_study`	No
`open_access_pdf`	No
`publication_types`	No
`min_citation_count`	No
`publication_date_or_year`	No

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.2.3Sep 18, 2026

TDQS

B3/5.0

Behavior3/5

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

With no annotations, the description must carry behavioral disclosure. It does disclose important limits (100 per call, 1000 total), which is useful. However, it omits other behavioral traits such as authentication requirements, side effects, or that it's a read operation, leaving gaps.

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?

Two sentences with no fluff, purpose front-loaded, and the limit context stated efficiently. Excellent structure for the information provided.

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

Completeness2/5

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

The tool has 11 parameters with no schema descriptions and no output schema details in the description. The only contextual info is the result limits. Given the complexity, this is incomplete for correct usage; an agent would struggle with valid values for fields like publication_types or fields_of_study.

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

Parameters2/5

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

Schema description coverage is 0%, so the description must compensate for parameter meaning. It provides no information about any of the 11 parameters, leaving agents to guess from names alone. This is a significant gap.

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

Purpose4/5

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

The description clearly states the tool runs a relevance-ranked /paper/search request, specifying the verb and resource. It distinguishes from siblings through the 'relevance-ranked' qualifier, though it does not explicitly name the alternative search tool.

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

Usage Guidelines2/5

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

No guidance is given on when to use this tool versus alternatives like search_papers. The description only mentions limits, not the selection criteria based on relevance needs.

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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