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LinXueyuanStdio

Academic MCP

by LinXueyuanStdio
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Python
Hybrid

πŸ“š Academic MCP

English | δΈ­ζ–‡

πŸ”¬ academic-mcp is a Python-based MCP server that enables users to search, download, and read academic papers from various platforms. It provides three main tools:

  • πŸ”Ž paper_search: Search papers across multiple academic databases

  • πŸ“₯ paper_download: Download paper PDFs, return paths of downloaded files

  • πŸ“– paper_read: Extract and read text content from papers

PyPI License Python

πŸ“‘ Table of Contents

Related MCP server: Paper Search MCP Server

✨ Features

  • 🌐 Multi-Source Support: Search and download papers from 19+ academic databases including arXiv, PubMed, PubMed Central, bioRxiv, medRxiv, Google Scholar, IACR ePrint Archive, Semantic Scholar, CrossRef, Science Direct, Springer, IEEE Xplore, Scopus, CORE, and more.

  • 🎯 Unified Interface: All platforms accessible through consistent paper_search, paper_download, and paper_read tools.

  • πŸ“Š Standardized Output: Papers are returned in a consistent dictionary format via the Paper class.

  • ⚑ Asynchronous Operations: Efficiently handles concurrent searches and downloads using httpx and async/await.

  • πŸ”Œ MCP Integration: Compatible with MCP clients for LLM context enhancement.

  • 🧩 Extensible Design: Easily add new academic platforms by extending the sources module.

🎬 Screenshot

πŸ“ Supported Academic Platforms

βœ… Fully Implemented (19 sources)

Free & Open Access:

  • arXiv - Pre-print repository for physics, mathematics, CS, and more

  • PubMed - Biomedical literature database

  • PubMed Central (PMC) - Free full-text biomedical and life sciences articles

  • bioRxiv - Pre-print server for biology

  • medRxiv - Pre-print server for health sciences

  • Semantic Scholar - AI-powered research tool

  • CrossRef - DOI registration agency and metadata provider

  • Google Scholar - Academic search engine

  • IACR ePrint Archive - Cryptology pre-prints

  • CORE - Open access research papers aggregator

API Key Required:

  • Science Direct - Elsevier's full-text scientific database (requires Elsevier API key)

  • Springer Link - Springer's scientific publications (requires Springer API key)

  • IEEE Xplore - IEEE's digital library (requires IEEE API key)

  • Scopus - Elsevier's abstract and citation database (requires Scopus API key)

Institutional Access Required:

  • ACM Digital Library - ACM's computing literature (no public API)

  • Web of Science - Clarivate's citation database (requires subscription)

  • JSTOR - Digital library of academic journals (no public API)

  • ResearchGate - Academic social network (no official API)

Retired Services:

  • Microsoft Academic - Service retired December 31, 2021 (placeholder implementation)

πŸ“¦ Installation

academic-mcp can be installed using uv or pip. Below are detailed installation guides for different scenarios.

⚑ Quick Install

Install the package:

pip install academic-mcp

Start the MCP server:

academic-mcp

πŸ”§ MCP Client Configuration

Choose your MCP client and follow the configuration steps:

Location:

  • macOS: ~/Library/Application Support/Claude/claude_desktop_config.json

  • Windows: %APPDATA%\Claude\claude_desktop_config.json

Configuration:

{
  "mcpServers": {
    "academic-mcp": {
      "command": "python",
      "args": ["-m", "academic_mcp"],
      "env": {
        "SEMANTIC_SCHOLAR_API_KEY": "",
        "SCIENCEDIRECT_API_KEY": "",
        "SPRINGER_API_KEY": "",
        "IEEE_API_KEY": "",
        "SCOPUS_API_KEY": "",
        "CORE_API_KEY": "",
        "WOS_API_KEY": "",
        "ACADEMIC_MCP_ENABLED_SOURCES": "arxiv,pubmed,pmc,biorxiv,medrxiv,semantic,core,crossref,google_scholar,iacr",
        "ACADEMIC_MCP_DISABLED_SOURCES": "ieee,scopus,springer,sciencedirect,wos,acm,jstor",
        "ACADEMIC_MCP_DOWNLOAD_PATH": "./downloads"
      }
    }
  }
}

Location:

  • User-level: ~/.claude/settings.json

  • Project-level: .mcp.json

Configuration:

{
  "mcpServers": {
    "academic-mcp": {
      "command": "academic-mcp",
      "args": [],
      "env": {
        "SEMANTIC_SCHOLAR_API_KEY": "",
        "SCIENCEDIRECT_API_KEY": "",
        "SPRINGER_API_KEY": "",
        "IEEE_API_KEY": "",
        "SCOPUS_API_KEY": "",
        "CORE_API_KEY": "",
        "WOS_API_KEY": "",
        "ACADEMIC_MCP_ENABLED_SOURCES": "arxiv,pubmed,pmc,biorxiv,medrxiv,semantic,core,crossref,google_scholar,iacr",
        "ACADEMIC_MCP_DISABLED_SOURCES": "ieee,scopus,springer,sciencedirect,wos,acm,jstor",
        "ACADEMIC_MCP_DOWNLOAD_PATH": "./downloads"
      }
    }
  }
}

Note: You can also use "command": "python" with "args": ["-m", "academic_mcp"] if you prefer running via Python module.

Verify Installation:

# Check if academic-mcp is loaded
claude mcp list

# Test the server
claude mcp test academic-mcp

Location: VS Code Settings β†’ Extensions β†’ Cline β†’ MCP Settings

Method 1: Through VS Code Settings UI

  1. Open VS Code Settings (Cmd/Ctrl + ,)

  2. Search for "Cline MCP"

  3. Click "Edit in settings.json"

  4. Add the configuration:

{
  "cline.mcpServers": {
    "academic-mcp": {
      "command": "academic-mcp",
      "args": [],
      "env": {
        "SEMANTIC_SCHOLAR_API_KEY": "",
        "SCIENCEDIRECT_API_KEY": "",
        "SPRINGER_API_KEY": "",
        "IEEE_API_KEY": "",
        "SCOPUS_API_KEY": "",
        "CORE_API_KEY": "",
        "WOS_API_KEY": "",
        "ACADEMIC_MCP_ENABLED_SOURCES": "arxiv,pubmed,pmc,biorxiv,medrxiv,semantic,core,crossref,google_scholar,iacr",
        "ACADEMIC_MCP_DISABLED_SOURCES": "ieee,scopus,springer,sciencedirect,wos,acm,jstor",
        "ACADEMIC_MCP_DOWNLOAD_PATH": "./downloads"
      }
    }
  }
}

Method 2: Direct settings.json Edit

Edit ~/.config/Code/User/settings.json (Linux/macOS) or %APPDATA%\Code\User\settings.json (Windows):

{
  "cline.mcpServers": {
    "academic-mcp": {
      "command": "academic-mcp",
      "args": [],
      "env": {
        "SEMANTIC_SCHOLAR_API_KEY": "",
        "SCIENCEDIRECT_API_KEY": "",
        "SPRINGER_API_KEY": "",
        "IEEE_API_KEY": "",
        "SCOPUS_API_KEY": "",
        "CORE_API_KEY": "",
        "WOS_API_KEY": "",
        "ACADEMIC_MCP_ENABLED_SOURCES": "arxiv,pubmed,pmc,biorxiv,medrxiv,semantic,core,crossref,google_scholar,iacr",
        "ACADEMIC_MCP_DISABLED_SOURCES": "ieee,scopus,springer,sciencedirect,wos,acm,jstor",
        "ACADEMIC_MCP_DOWNLOAD_PATH": "./downloads"
      }
    }
  }
}

Location: ~/.config/zed/settings.json

Configuration:

{
  "context_servers": {
    "academic-mcp": {
      "command": {
        "path": "academic-mcp",
        "args": []
      },
      "settings": {
        "env": {
          "SEMANTIC_SCHOLAR_API_KEY": "",
          "SCIENCEDIRECT_API_KEY": "",
          "SPRINGER_API_KEY": "",
          "IEEE_API_KEY": "",
          "SCOPUS_API_KEY": "",
          "CORE_API_KEY": "",
          "WOS_API_KEY": "",
          "ACADEMIC_MCP_ENABLED_SOURCES": "arxiv,pubmed,pmc,biorxiv,medrxiv,semantic,core,crossref,google_scholar,iacr",
          "ACADEMIC_MCP_DISABLED_SOURCES": "ieee,scopus,springer,sciencedirect,wos,acm,jstor",
          "ACADEMIC_MCP_DOWNLOAD_PATH": "./downloads"
        }
      }
    }
  }
}

For other MCP clients, use the standard MCP server configuration:

Server Command:

academic-mcp
# or
python -m academic_mcp

Environment Variables:

  • SEMANTIC_SCHOLAR_API_KEY: Optional API key for Semantic Scholar

  • SCIENCEDIRECT_API_KEY: Optional API key for Science Direct

  • SPRINGER_API_KEY: Optional API key for Springer Link

  • IEEE_API_KEY: Optional API key for IEEE Xplore

  • SCOPUS_API_KEY: Optional API key for Scopus

  • CORE_API_KEY: Optional API key for CORE

  • WOS_API_KEY: Optional API key for Web of Science

  • ACADEMIC_MCP_DOWNLOAD_PATH: Download directory (default: ./downloads)

  • ACADEMIC_MCP_ENABLED_SOURCES: Comma-separated list of enabled sources

  • ACADEMIC_MCP_DISABLED_SOURCES: Comma-separated list of disabled sources

Server Capabilities:

  • Tools: paper_search, paper_download, paper_read

  • Transport: stdio

  • Protocol: MCP 1.0

βš™οΈ Environment Variables

API Keys (optional - only for premium services):

  • SEMANTIC_SCHOLAR_API_KEY: Semantic Scholar (Get API Key)

  • SCIENCEDIRECT_API_KEY: Elsevier Science Direct (Get API Key)

  • SPRINGER_API_KEY: Springer Nature (Get API Key)

  • IEEE_API_KEY: IEEE Xplore (Get API Key)

  • SCOPUS_API_KEY: Elsevier Scopus (Get API Key)

  • CORE_API_KEY: CORE aggregator (Get API Key)

  • WOS_API_KEY: Web of Science (requires institutional subscription)

General Settings:

  • ACADEMIC_MCP_DOWNLOAD_PATH: Directory for downloaded PDFs (default: ./downloads)

Source Control:

  • ACADEMIC_MCP_ENABLED_SOURCES: Comma-separated list to enable specific sources (whitelist)

  • ACADEMIC_MCP_DISABLED_SOURCES: Comma-separated list to disable specific sources (blacklist)

  • If both are set, ACADEMIC_MCP_ENABLED_SOURCES takes precedence

  • If neither is set, all 18 sources are enabled by default

Available Source Names (18 total):

Source Name

Type

API Key Required

Description

arxiv

Free

-

Preprint repository for physics, mathematics, computer science

pubmed

Free

-

Biomedical literature from MEDLINE

pmc

Free

-

PubMed Central full-text archive

biorxiv

Free

-

Preprint server for biology

medrxiv

Free

-

Preprint server for health sciences

google_scholar

Free

-

Google Scholar search

iacr

Free

-

International Association for Cryptologic Research

semantic

Free

SEMANTIC_SCHOLAR_API_KEY (optional)Get API Key

Semantic Scholar AI-powered search (higher rate limits with API key)

crossref

Free

-

Crossref DOI metadata

core

Free

CORE_API_KEYGet API Key

CORE aggregator of open access papers

ieee

Premium

IEEE_API_KEYGet API Key

IEEE Xplore digital library

scopus

Premium

SCOPUS_API_KEYGet API Key

Elsevier Scopus database

springer

Premium

SPRINGER_API_KEYGet API Key

Springer publications

sciencedirect

Premium

SCIENCEDIRECT_API_KEYGet API Key

Elsevier ScienceDirect

wos

Premium

WOS_API_KEYInstitutional Access

Web of Science (requires institutional subscription)

acm

Premium

-

ACM Digital Library

jstor

Premium

-

JSTOR archive

researchgate

Free

-

ResearchGate social network

πŸš€ Usage

Once configured, academic-mcp provides three main tools accessible through Claude Desktop or any MCP-compatible client.

1. Search Papers (paper_search)

Search for academic papers across multiple sources:

Basic Search Examples:

# Search arXiv for machine learning papers
paper_search([
    {"searcher": "arxiv", "query": "machine learning", "max_results": 5}
])

# Search PubMed Central for biomedical papers
paper_search([
    {"searcher": "pmc", "query": "cancer treatment", "max_results": 5}
])

# Search CORE for open access papers
paper_search([
    {"searcher": "core", "query": "climate change", "max_results": 5}
])

Multi-Platform Search:

# Search multiple platforms simultaneously
paper_search([
    {"searcher": "arxiv", "query": "deep learning", "max_results": 5},
    {"searcher": "pubmed", "query": "cancer immunotherapy", "max_results": 3},
    {"searcher": "pmc", "query": "diabetes treatment", "max_results": 3},
    {"searcher": "semantic", "query": "climate change", "max_results": 4, "year": "2020-2023"}
])

Premium Sources (require API keys):

# Search IEEE Xplore (requires IEEE_API_KEY)
paper_search([
    {"searcher": "ieee", "query": "neural networks", "max_results": 5}
])

# Search Springer Link (requires SPRINGER_API_KEY)
paper_search([
    {"searcher": "springer", "query": "quantum computing", "max_results": 5}
])

# Search Scopus (requires SCOPUS_API_KEY)
paper_search([
    {"searcher": "scopus", "query": "artificial intelligence", "max_results": 5}
])

Search All Platforms:

# Search all platforms (omit "searcher" parameter)
paper_search([
    {"query": "quantum computing", "max_results": 10}
])

2. Download Papers (paper_download)

Download paper PDFs using their identifiers:

# Download from free sources
paper_download([
    {"searcher": "arxiv", "paper_id": "2106.12345"},
    {"searcher": "pubmed", "paper_id": "32790614"},
    {"searcher": "pmc", "paper_id": "PMC7419405"},
    {"searcher": "biorxiv", "paper_id": "10.1101/2020.01.01.123456"},
    {"searcher": "semantic", "paper_id": "DOI:10.18653/v1/N18-3011"}
])

# Download from CORE (open access)
paper_download([
    {"searcher": "core", "paper_id": "123456789"}
])

Note: Premium sources (IEEE, Springer, Science Direct, Scopus) require institutional access or subscriptions for PDF downloads.

3. Read Papers (paper_read)

Extract and read text content from papers:

# Read papers from free sources
paper_read(searcher="arxiv", paper_id="2106.12345")
paper_read(searcher="pubmed", paper_id="32790614")
paper_read(searcher="pmc", paper_id="PMC7419405")
paper_read(searcher="biorxiv", paper_id="10.1101/2020.01.01.123456")
paper_read(searcher="semantic", paper_id="DOI:10.18653/v1/N18-3011")
paper_read(searcher="core", paper_id="123456789")

πŸ› οΈ For Development

For developers who want to modify the code or contribute:

  1. Setup Environment:

    # Install uv if not installed
curl -LsSf https://astral.sh/uv/install.sh | sh

# Clone repository
git clone https://github.com/LinXueyuanStdio/academic-mcp.git
cd academic-mcp

# Create and activate virtual environment
uv venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

  2. Install Dependencies:

    # Install dependencies (recommended)
uv pip install -e .

# Add development dependencies (optional)
uv pip install pytest flake8

🀝 Contributing

We welcome contributions! Here's how to get started:

  1. Fork the Repository: Click "Fork" on GitHub.

  2. Clone and Set Up:

    git clone https://github.com/yourusername/academic-mcp.git
cd academic-mcp
uv pip install -e .  # Install in development mode

  3. Make Changes:

    • Add new platforms in academic_mcp/sources/.

    • Update tests in tests/.

  4. Submit a Pull Request: Push changes and create a PR on GitHub.

πŸ“„ License

This project is licensed under the MIT License. See the LICENSE file for details.

Happy researching with academic-mcp! If you encounter issues, open a GitHub issue.

Available Tools

3 tools
paper_downloadA

Download academic paper PDFs from multiple sources.

Input Constraints:

  • searcher: Required, must be one of the supported platforms

  • paper_id: Required, 1-200 characters, cannot be empty

Paper ID formats:

  • arXiv: Use the arXiv ID (e.g., "2106.12345").

  • PubMed: Use the PubMed ID (PMID) (e.g., "32790614").

  • bioRxiv: Use the bioRxiv DOI (e.g., "10.1101/2020.01.01.123456").

  • medRxiv: Use the medRxiv DOI (e.g., "10.1101/2020.01.01.123456").

  • Google Scholar: Direct PDF download is not supported; please use the paper URL to access the publisher's website.

  • IACR: Use the IACR paper ID (e.g., "2009/101").

  • Semantic Scholar: Use the Semantic Scholar paper ID, Paper identifier in one of the following formats:

    • Semantic Scholar ID (e.g., "649def34f8be52c8b66281af98ae884c09aef38b")

    • DOI: (e.g., "DOI:10.18653/v1/N18-3011")

    • ARXIV: (e.g., "ARXIV:2106.15928")

    • MAG: (e.g., "MAG:112218234")

    • ACL: (e.g., "ACL:W12-3903")

    • PMID: (e.g., "PMID:19872477")

    • PMCID: (e.g., "PMCID:2323736")

    • URL: (e.g., "URL:https://arxiv.org/abs/2106.15928v1")

Returns:

List of paths to the downloaded PDF files.

Example:

paper_download([ {"searcher": "arxiv", "paper_id": "2106.12345"}, {"searcher": "pubmed", "paper_id": "32790614"}, {"searcher": "biorxiv", "paper_id": "10.1101/2020.01.01.123456"}, {"searcher": "semantic", "paper_id": "DOI:10.18653/v1/N18-3011"} ])

ParametersJSON Schema
NameRequiredDescriptionDefault
query_listYes

Output Schema

ParametersJSON Schema
NameRequiredDescription
resultYes

TDQS

A4.5/5.0
Behavior4/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes what the tool does (downloads PDFs), mentions platform-specific constraints (Google Scholar limitation), and specifies the return format ('List of paths to the downloaded PDF files'). It doesn't cover potential errors, rate limits, or authentication needs, but provides substantial operational context.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with clear sections (Input Constraints, Paper ID formats, Returns, Example) and front-loaded with the core purpose. While comprehensive, some details like the extensive Semantic Scholar formats could be slightly condensed, but overall it's efficient and informative.

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 the complexity of multiple academic sources with different ID formats, no annotations, and an output schema (which handles return values), the description is complete. It covers purpose, usage constraints, parameter details with examples, return information, and includes a practical example, leaving no significant gaps for the agent.

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?

The schema description coverage is 0%, so the description must fully compensate. It provides extensive parameter semantics: it explains the 'searcher' parameter with supported platforms and format requirements for 'paper_id' across multiple sources, including detailed examples. This adds crucial meaning beyond what the bare schema provides.

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's purpose: 'Download academic paper PDFs from multiple sources.' This is a specific verb ('download') + resource ('academic paper PDFs') + scope ('from multiple sources'), and it distinguishes from sibling tools like 'paper_read' and 'paper_search' which likely have different functions.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear context for when to use this tool (to download PDFs from academic sources), and it explicitly mentions that Google Scholar doesn't support direct PDF downloads, suggesting an alternative approach. However, it doesn't explicitly compare usage with sibling tools like 'paper_search' or specify when not to use it.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

paper_readA

Read and extract text content from academic paper PDFs from multiple sources.

Input Constraints:

  • searcher: Required, must be one of: arxiv, pubmed, biorxiv, medrxiv, iacr, semantic, crossref

  • paper_id: Required, 1-200 characters, cannot be empty

Example:

arXiv

paper_read({"searcher": "arxiv", "paper_id": "2106.12345", "save_path": "./downloads"}) # paper_id is arXiv ID.

PubMed

paper_read({"searcher": "pubmed", "paper_id": "32790614", "save_path": "./downloads"}) # paper_id is PubMed ID (PMID).

bioRxiv

paper_read({"searcher": "biorxiv", "paper_id": "10.1101/2020.01.01.123456", "save_path": "./downloads"}) # paper_id is bioRxiv DOI.

medRxiv

paper_read({"searcher": "medrxiv", "paper_id": "10.1101/2020.01.01.123456", "save_path": "./downloads"}) # paper_id is medRxiv DOI.

IACR

paper_read({"searcher": "iacr", "paper_id": "2009/101", "save_path": "./downloads"}) # paper_id is IACR paper ID.

Semantic Scholar

paper_read({"searcher": "semantic", "paper_id": "DOI:10.18653/v1/N18-3011", "save_path": "./downloads"}) where paper_id: Semantic Scholar paper ID, Paper identifier in one of the following formats: - Semantic Scholar ID (e.g., "649def34f8be52c8b66281af98ae884c09aef38b") - DOI: (e.g., "DOI:10.18653/v1/N18-3011") - ARXIV: (e.g., "ARXIV:2106.15928") - MAG: (e.g., "MAG:112218234") - ACL: (e.g., "ACL:W12-3903") - PMID: (e.g., "PMID:19872477") - PMCID: (e.g., "PMCID:2323736") - URL: (e.g., "URL:https://arxiv.org/abs/2106.15928v1")

CrossRef

paper_read({"searcher": "crossref", "paper_id": "10.1038/s41586-020-2649-2", "save_path": "./downloads"}) # paper_id is DOI.

ParametersJSON Schema
NameRequiredDescriptionDefault
searcherYes
paper_idYesThe unique identifier of the paper to read (format depends on searcher)

Output Schema

ParametersJSON Schema
NameRequiredDescription
resultYes

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 carries the full burden. It describes the tool's behavior (reading and extracting text from PDFs) and includes detailed input constraints and examples, but it does not disclose other behavioral traits like error handling, rate limits, authentication needs, or what happens if extraction fails. The description adds value but lacks comprehensive behavioral context.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is appropriately front-loaded with the core purpose, but it includes lengthy examples and formatting that could be condensed. While informative, the structure with bullet points and code blocks is somewhat verbose, reducing efficiency. Every sentence earns its place, but it could be more streamlined.

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 tool's complexity (multiple sources with different ID formats), the description is quite complete, with detailed examples and input constraints. Since an output schema exists, the description does not need to explain return values. However, it lacks information on error cases or performance limits, leaving minor gaps.

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 50% (only 'paper_id' has a description), but the description compensates fully by providing extensive parameter semantics. It explains the meaning of 'searcher' (listing valid sources) and 'paper_id' (with format details and examples for each searcher), adding significant value beyond the input 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 states the tool's purpose as 'Read and extract text content from academic paper PDFs from multiple sources,' which is a specific verb (read/extract) applied to a specific resource (academic paper PDFs). It distinguishes from sibling tools 'paper_download' and 'paper_search' by focusing on content extraction rather than downloading or searching.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear context for when to use this tool (for reading/extracting text from academic papers) and implies alternatives through sibling tool names, but it does not explicitly state when to choose this tool over 'paper_download' or 'paper_search.' The examples show specific use cases for different sources, which helps guide 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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TDQS

A3.9/5.0
Disambiguation5/5

The three tools have clearly distinct purposes: paper_download downloads PDFs, paper_read extracts text from PDFs, and paper_search searches for papers. There is no overlap in functionality, and an agent can easily differentiate between them based on their names and descriptions.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern with 'paper_' as a prefix: paper_download, paper_read, and paper_search. This naming convention is uniform and predictable throughout the set.

Tool Count3/5

With only 3 tools, the server feels thin for the broad domain of academic paper management. While the tools cover basic operations (search, download, read), the scope suggests more could be included, such as tools for metadata retrieval, citation management, or paper summarization, making the count borderline for the apparent purpose.

Completeness3/5

The tools cover search, download, and text extraction, which are core operations, but there are notable gaps. For example, there is no tool for updating or managing paper metadata, handling citations, or performing advanced analyses like summarization or keyword extraction, which are common in academic workflows.

Maintenance

ActivityMaintained
ResponsivenessSyncing

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    quality
    D
    maintenance
    Enables searching and downloading academic papers from multiple sources including arXiv, PubMed, bioRxiv, Google Scholar, and Semantic Scholar. Provides standardized tools compatible with OpenAI Deep Research and ChatGPT connectors.
    14
    MIT

  • Paper Search MCP Server

    h-lu
    F
    license
    A
    quality
    C
    maintenance
    Enables searching, downloading, and reading academic papers from multiple platforms including arXiv, Semantic Scholar, PubMed, bioRxiv, medRxiv, IACR, Google Scholar, RePEc/IDEAS, and Sci-Hub with PDF to Markdown conversion.
    29
    7

  • Paper Search MCP

    Dianel555
    A
    license
    B
    quality
    B
    maintenance
    Enables searching and downloading academic papers from 14 platforms including arXiv, PubMed, Google Scholar, Web of Science, Springer, and Sci-Hub with unified data format and intelligent rate limiting.
    19
    931
    182
    MIT

  • paper-search

    its-antony
    A
    license
    Not graded
    quality
    D
    maintenance
    Enables searching, downloading, and exporting academic papers from 20+ scholarly sources including arXiv, PubMed, and Semantic Scholar. Supports multi-source concurrent search, citation network tracing, and export to CSV, RIS, and BibTeX.
    MIT

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