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h-lu

Paper Search MCP Server

by h-lu
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

read_scihub_paper

Download and extract full text of pre-2023 academic papers from Sci-Hub as Markdown with metadata. Requires a DOI.

Instructions

Download and extract full text from paper via Sci-Hub (older papers only).

USE THIS TOOL WHEN:
- You need the complete text content of a paper (not just abstract)
- The paper was published BEFORE 2023
- You want to analyze, summarize, or answer questions about a paper

This downloads the PDF and extracts text as clean Markdown format,
suitable for LLM processing. Includes paper metadata at the start.

WORKFLOW: search_crossref(query) -> get DOI -> read_scihub_paper(doi)

Args:
    doi: Paper DOI (e.g., '10.1038/nature12373').
    save_path: Directory to save PDF (default: ~/paper_downloads).

Returns:
    Full paper text in Markdown format with metadata header,
    or error message if download/extraction fails.

Example:
    read_scihub_paper("10.1038/nature12373")

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
doiYes
save_pathNo

Tool Definition Quality

A4.7/5.0
Behavior4/5

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

Describes the download and text extraction process, output format (Markdown with metadata), and error handling. No annotations, but description covers key behavioral aspects without contradiction.

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?

Well-structured with sections for use, workflow, args, returns, and example. Every sentence is informative, no 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?

Complete coverage for a tool with 2 parameters and no output schema: purpose, usage guidelines, parameters, return format, and example workflow.

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

Parameters4/5

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

Despite 0% schema description coverage, the description explains parameters: doi with example, save_path with default. Adds meaning beyond raw schema.

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 specifies 'Download and extract full text from paper via Sci-Hub (older papers only)', providing a specific verb and resource. It distinguishes from siblings by mentioning Sci-Hub and date restriction.

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?

Explicitly lists 'USE THIS TOOL WHEN' conditions: need full text, paper before 2023, want to analyze. Includes workflow referencing search_crossref, guiding proper usage.

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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