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Paper Search MCP

Paper Search MCP

by openags
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

download_scihub

Download academic paper PDFs from Sci-Hub using DOI, title, PMID, or URL. Specify save directory and optional mirror URL for access to research papers.

Instructions

Download paper PDF via Sci-Hub (optional fallback connector).

Args: identifier: DOI, title, PMID, or paper URL. save_path: Directory to save the PDF. base_url: Sci-Hub mirror URL. Returns: Downloaded PDF path on success; error message on failure.

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": {
      "identifier": "<identifier>"
    },
    "name": "download_scihub"
  }
}

Input Schema

NameRequiredDescriptionDefault
identifierYes
save_pathNo./downloads
base_urlNohttps://sci-hub.se

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.4

TDQS

A4.1/5.0
Behavior3/5

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

No annotations are provided, so the description bears the full burden. It mentions 'optional fallback connector' and returns 'downloaded PDF path on success; error message on failure', adding some behavioral context. However, it does not disclose side effects (e.g., file system writes), network requirements, or potential legal/access issues with Sci-Hub.

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 short (one sentence plus bullet-like args and return) with no wasted words. It is front-loaded with the core purpose, making it easy to scan.

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?

Given the presence of an output schema (not shown), the description covers the essential aspects: purpose, parameters, and return value. It could be more complete by elaborating on the fallback behavior or prerequisites, but overall it is adequate for a simple download tool.

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 compensates fully. It explains each parameter: identifier (DOI, title, PMID, or paper URL), save_path (directory), and base_url (Sci-Hub mirror URL), adding meaning beyond the schema titles and defaults. The explicit formats for identifier are especially helpful.

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 'Download paper PDF via Sci-Hub', which is a specific verb and resource. The tool name and description distinguish it from many sibling download tools (e.g., download_arxiv) by naming the source Sci-Hub. The optional fallback connector adds minor context but does not hinder clarity.

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 does not explicitly state when to use this tool over alternatives. Usage is implied by the source name 'Sci-Hub', but no guidance is given on whether to use fallback or when not to use it. The sibling tools have similar structures, so the agent must infer context from the name alone.

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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