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semantic-scholar-mcp

semantic-scholar-mcp

by ckgerteis
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

s2_recommend_single

Read-onlyIdempotent

Find papers similar to a seed paper by entering its ID; returns a ranked list of recommendations from Semantic Scholar, with configurable limit up to 100.

Instructions

Papers similar to one seed paper, by Semantic Scholar's recommender. Returns the unified envelope (matching_mode similarity_ranked).

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "params": "<params>"
    },
    "name": "s2_recommend_single"
  }
}

Input Schema

NameRequiredDescriptionDefault
paramsYes

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.1.0

TDQS

A3.5/5.0
Behavior3/5

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

Annotations already cover read-only, idempotent, non-destructive, and open-world behavior. The description adds that the result is a 'unified envelope (matching_mode similarity_ranked)', but this phrase is cryptic and does not meaningfully explain output behavior or limitations beyond what annotations already provide.

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

Conciseness4/5

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

The description is short and front-loaded: the core behavior appears in the first sentence. The second sentence adds output-mode terminology, but it is compact and does not bloat the definition, even if it could be clearer.

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

Completeness3/5

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

For a simple single-seed recommendation tool, the description is almost sufficient, and the output schema plus annotations carry some burden. It is missing explicit guidance on the `limit` semantics and on when to prefer s2_recommend_multi, so an agent would still need to infer some important call decisions.

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?

The description only mentions the seed-paper concept and gives no guidance about the `limit` parameter or how the recommendation set is influenced by it. With the reported low schema description coverage, the description does not compensate by explaining parameter roles, though the input schema itself does contain basic definitions for `paper_id` and `limit`.

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 that the tool returns papers similar to one seed paper using Semantic Scholar's recommender, which is a specific resource-and-action pair. It also distinguishes itself from s2_recommend_multi by emphasizing the single-seed-paper input.

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

Usage Guidelines3/5

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

The description implies this is for recommendation from a single seed paper, which gives some context about when to use it. However, it does not explicitly tell the agent when to choose this over s2_recommend_multi or when not to use it, leaving the routing partly to inference.

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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