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Liyux3

scholar-mcp

by Liyux3
OverviewSchemaSourceRelated ServersScoreDiscussionsAdmin
Python
Remote

Recommend related papers

recommend_papers
Read-onlyIdempotent

Find related papers around a known paper using citation-graph relations: similar topics, peer works, or shared cited methods. Choose the relation that fits your research question and get relevant literature connected to your starting paper.

Instructions

Find related papers by a chosen citation-graph relation.

Use this when you hold one paper and want its neighbourhood. Use search_papers for a topic and paper_info for a paper's direct citation lists. Read-only; results come from embedding and citation data, so very new or uncited papers return few or no neighbours.

"Related" is several different questions, and which one you want depends on what you are doing:

similar     embedding neighbours (SPECTER2). Same topic, possibly
            different vocabulary. Good default.
peers       what is cited alongside this paper. Its intellectual
            cohort, which is usually what "related work" means.
kin         what cites the same works this paper does. Shared method
            rather than shared topic, so this is the relation that
            crosses field boundaries: two papers can be coupled
            without sharing any vocabulary.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMaximum results (default 10)
paper_idYesPaper identifier (S2 ID, DOI, ArXiv:ID, OpenAlex ID, etc.)
relationNosimilar | peers | kinsimilar

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed6 schema fields changedv0.8.3
    • changedInput schema / properties / limit / description
      Previous value: -"Maximum recommendations (1-500, default 10)"New value: +"Maximum results (default 10)"
    • addedInput schema / properties / relation
      Added value: +{
+  "default": "similar",
+  "description": "similar | peers | kin",
+  "type": "string"
+}
    • addedOutput schema / additionalProperties
      Added value: +true
    • removedOutput schema / properties
      Removed value: -{
-  "result": {
-    "type": "string"
-  }
-}
    • removedOutput schema / required
      Removed value: -[
-  "result"
-]
    • removedOutput schema / x-fastmcp-wrap-result
      Removed value: -true
  2. First observedv0.6.0

TDQS

A5/5.0
Behavior5/5

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

Annotations already mark the tool read-only, idempotent, and non-destructive. The description adds genuine behavioral context beyond those flags: results come from embedding and citation data, so 'very new or uncited papers return few or no neighbours.' It also explains the meaningful behavioral differences among the three relation types, which is not visible from annotations alone.

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 moderately long but every section earns its place: a one-sentence purpose, explicit routing to alternatives, a data-source caveat, and a structured explanation of the three relation types. It is front-loaded with the core purpose and usage guidance before diving into parameter semantics, with no filler.

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?

For a tool with three relation modes and an output schema, the description is complete. It covers when to use the tool, how to choose between relations, data limitations, and the meaning of each parameter. Because an output schema is present, the absence of return-format details does not hurt completeness.

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

Parameters5/5

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

The schema description coverage is 100%, so the baseline is 3. However, the description goes well beyond the schema by explaining what each relation value means semantically: similar, peers, and kin each get a clear definition with practical implications. This is especially valuable because the schema merely lists 'similar | peers | kin' with no per-enum descriptions.

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 opens with a specific verb and resource: 'Find related papers by a chosen citation-graph relation.' It clearly distinguishes this tool from siblings by explicitly naming search_papers and paper_info as the alternatives for different needs, so an agent can immediately tell what recommend_papers is and is not for.

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?

It gives explicit when-to-use guidance: 'Use this when you hold one paper and want its neighbourhood.' It also names alternatives and their conditions: 'Use search_papers for a topic and paper_info for a paper's direct citation lists.' This leaves no ambiguity about tool selection.

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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