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semanticscholar-mcp

semanticscholar-mcp

by xbghc
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Remote

Get Paper Recommendations

get_recommendations

Finds similar academic papers based on specified paper IDs, optionally excluding papers similar to negative examples.

Instructions

基于指定论文获取推荐的相关论文

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": {
      "positivePaperIds": "<positivePaperIds>"
    },
    "name": "get_recommendations"
  }
}

Input Schema

NameRequiredDescriptionDefault
limitNo推荐数量，最大 500
negativePaperIdsNo负向参考论文 ID 列表（想要避免类似的论文）
positivePaperIdsYes正向参考论文 ID 列表（想要找类似的论文）

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.1

TDQS

A3.5/5.0
Behavior2/5

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

With no annotations, the description carries the full burden. It only states the basic function without disclosing how recommendations are generated, whether they are content-based or citation-based, or what limits apply. It does not mention any side effects, permissions, or output characteristics, leaving significant behavioral ambiguity.

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?

The description is a single, concise sentence that immediately states the core function. It is front-loaded and contains no unnecessary words or repetition.

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?

The tool is relatively simple with only three parameters, all well-documented in the schema. However, there is no output schema and no annotations, and the description does not mention return format, default behavior, or any edge cases. It is minimally viable but leaves notable gaps for an agent seeking full context.

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

Parameters3/5

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

The schema description coverage is 100%, as all parameters have descriptive Chinese labels explaining their purpose. The tool description itself adds no additional parameter semantics beyond the schema, so the baseline of 3 is appropriate.

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 uses a specific verb ('获取' / 'get') and resource ('推荐的相关论文' / 'recommended related papers') based on specified papers. It clearly distinguishes this from sibling tools like get_paper_citations or get_paper_references, making it the only recommendation-oriented tool.

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 usage: when you have specific papers and want similar ones, use this tool. However, it provides no explicit guidance on when not to use it or how it differs from alternatives like get_paper_references. The context is clear but not elaborated.

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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