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openags

Paper Search MCP

by openags
OverviewSchemaRelated ServersScoreDiscussions
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.

Input Schema

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

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • paper_search_mcp/server.py:730-749 (handler)
    The `download_scihub` tool implementation in `server.py`, which is registered as an MCP tool using `@mcp.tool()`. It uses `SciHubFetcher` to download a PDF based on the provided identifier.
    @mcp.tool()
async def download_scihub(
    identifier: str,
    save_path: str = "./downloads",
    base_url: str = "https://sci-hub.se",
) -> str:
    """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.
    """
    fetcher = SciHubFetcher(base_url=base_url, output_dir=save_path)
    result = await asyncio.to_thread(fetcher.download_pdf, identifier)
    if result:
        return result
    return "Sci-Hub download failed. Try DOI first, then title, or change mirror URL."

Tool Definition Quality

B3.4/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions that the tool downloads PDFs via Sci-Hub with an optional fallback, but doesn't disclose critical behavioral traits such as potential legal/ethical considerations of using Sci-Hub, network reliability, rate limits, authentication needs, or what constitutes 'failure' beyond the generic error message. The description adds some context but leaves significant gaps for a tool with no annotation coverage.

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-structured and front-loaded with the core purpose, followed by clear sections for Args and Returns. Every sentence earns its place: the first sentence states the tool's function, and the subsequent lines efficiently document parameters and return values without redundancy. It's appropriately sized for a tool with three parameters.

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

Completeness3/5

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

Given the tool's complexity (downloads from Sci-Hub with fallback), lack of annotations, and schema coverage of 0%, the description is moderately complete. It explains parameters and return values (with an output schema present), but misses important contextual details like behavioral risks, performance characteristics, or sibling tool differentiation. For a tool with no annotations, it should do more to cover behavioral aspects.

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?

Schema description coverage is 0%, so the description must compensate. It provides clear semantics for all three parameters: 'identifier' as DOI, title, PMID, or paper URL; 'save_path' as directory to save the PDF; and 'base_url' as Sci-Hub mirror URL. This adds meaningful context beyond the bare schema, though it doesn't specify format details (e.g., DOI syntax) or constraints (e.g., valid URL patterns).

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Download paper PDF via Sci-Hub (optional fallback connector).' It specifies the action (download), resource (paper PDF), and mechanism (Sci-Hub with fallback). However, it doesn't explicitly differentiate from sibling tools like 'download_arxiv' or 'download_with_fallback' beyond mentioning Sci-Hub specifically.

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 implies usage context by mentioning 'Sci-Hub' and 'optional fallback connector,' suggesting it's for accessing papers potentially behind paywalls. However, it doesn't provide explicit guidance on when to use this tool versus alternatives like 'download_arxiv' for arXiv papers or 'download_with_fallback' for general fallback scenarios. No exclusions or prerequisites are stated.

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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