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Usama1002

Research Buddy MCP Server

by Usama1002
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Python
Remote

Get Related Papers

get_related_papers

Generate AI-driven paper recommendations from seed paper IDs; optionally steer away from negative examples and set a result limit.

Instructions

Get AI-driven paper recommendations based on seed papers.

  • paper_ids: List of paper IDs to base recommendations on (e.g. ['paperid1', 'paperid2']). Can be a single paper or multiple for more refined results.

  • negative_paper_ids: Optional list of paper IDs to use as negative examples. Recommendations will be steered AWAY from these papers. Useful for narrowing a search (e.g. 'find papers like A but not like B').

  • limit: Number of results (default 10).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
paper_idsYes
negative_paper_idsNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.1/5.0
Behavior3/5

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

With no annotations, the description carries the full behavioral burden. It implies a read-only operation and explains that negative_paper_ids steer recommendations away, but it does not disclose return format, permissions, rate limits, or output structure.

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 front-loaded with the core purpose and then uses compact bullets for each parameter. Every sentence adds useful information with no filler.

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

Completeness4/5

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

For a three-parameter tool with no annotations or output schema, the description covers purpose and parameters well. It still lacks sibling routing guidance and any description of the returned recommendation format, which would improve 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?

Schema description coverage is 0%, so the description must compensate, and it does. It documents all three parameters with meaning, optionality, default values, and usage examples for paper_ids and negative_paper_ids.

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 states a specific verb and resource: get AI-driven paper recommendations based on seed papers. The seed-paper basis distinguishes it from search_papers and other search siblings without needing to name them.

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?

It explains how to use paper_ids and negative_paper_ids, including a useful example for narrowing results. However, it does not say when to use this tool versus search_papers, bulk_search_papers, or other recommendation-adjacent siblings.

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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