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h-lu

Paper Search MCP Server

by h-lu

search_google_scholar

Search Google Scholar for academic papers across all disciplines. Retrieve titles, authors, citation counts, and abstracts for any research query.

Instructions

Search academic papers on Google Scholar (broad coverage).

USE THIS TOOL WHEN:
- You need broad academic search across ALL disciplines
- You want citation counts and "cited by" information
- Other specialized tools don't cover your topic

COVERAGE: All academic disciplines, books, theses, patents.

LIMITATIONS:
- Uses web scraping (may be rate-limited)
- Does NOT support PDF download

FOR FULL TEXT (try in order):
1. download_arxiv(id) - if arXiv preprint
2. download_scihub(doi) - if published before 2023
3. download_semantic(id) - last resort

Args:
    query: Search terms (any academic topic).
    max_results: Number of results (default: 10, keep small to avoid blocks).

Returns:
    List of paper dicts with: title, authors, abstract snippet,
    citations count, url, source.

Example:
    search_google_scholar("climate change economic impact", max_results=5)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYes
max_resultsNo
Behavior4/5

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

With no annotations, the description reveals web scraping usage, rate-limiting risks, and absence of PDF support. However, it lacks details on exact rate limits or error behavior on blocks.

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 well-organized with sections, bullet points, and an example. Every sentence adds value without redundancy.

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?

Given no output schema, the description details the return structure (list of dicts with specific fields) and includes an example. It also covers usage, limitations, and alternatives, making it highly complete for a search tool with two parameters.

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%, but the description gives meaningful explanations for both parameters: 'query' as any academic topic, and 'max_results' with advice to keep small. This fully compensates for the schema gap.

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 the tool searches academic papers on Google Scholar with broad coverage, distinguishing it from specialized sibling search tools like search_arxiv or search_pubmed.

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?

The description includes a dedicated 'USE THIS TOOL WHEN' section with three explicit criteria, provides limitations (rate limiting, no PDF download), and suggests alternative tools for full-text access in order.

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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