Paper Search MCP

A Model Context Protocol (MCP) server for searching and downloading academic papers from multiple sources. The project follows a free-first strategy: prioritize open and public data sources, support optional API keys when they improve stability or coverage, and keep source-specific connectors extensible for advanced users.

Table of Contents

• Overview

• Project Principles

• Features

• Source Strategy

• Sci-Hub Notice

• Installation

  • Method 1 — Smithery

  • Method 2 — uvx

  • Method 3 — uv

  • Method 4 — pip

  • Method 5 — npx

  • Method 6 — Docker

  • Method 7 — Clone & run from source

  • Environment Variables

• Contributing

• Demo

• Star History

• License

• TODO

Related MCP server: PubMed MCP Server

Overview

paper-search-mcp is a Python-based MCP server that enables users to search and download academic papers from various platforms. It provides tools for searching papers (e.g., search_arxiv) and downloading PDFs (e.g., download_arxiv), making it ideal for researchers and AI-driven workflows. Built with the MCP Python SDK, it integrates seamlessly with LLM clients like Claude Desktop.

Project Principles

• Free-First: Public and open sources are the default roadmap. Paid or restricted sources are not the core direction of this project.

• Optional API Keys: API keys are supported only when they improve stability, rate limits, or metadata quality. The MCP should still be usable without them whenever possible.

• LLM-Friendly Retrieval: Search results should be standardized, deduplicated, and as complete as possible for downstream LLM workflows.

• Source Transparency: Different sources have different strengths. The MCP should make those tradeoffs explicit instead of pretending every source supports full-text retrieval.

Features

• Two-Layer Architecture:

  • Layer 1 (Unified Tooling): High-level search_papers for multi-source concurrent search & deduplication, and download_with_fallback relying on publisher open access links with sequential fallbacks.

  • Layer 2 (Platform Connectors): Modular connectors for specific academic platforms (arXiv, PubMed, bioRxiv, Semantic Scholar, etc.) equipped with intelligent DOI extraction via regex text analysis or API fields.

• Multi-Source Support: Search and download papers from arXiv, PubMed, bioRxiv, medRxiv, Google Scholar, IACR ePrint Archive, Semantic Scholar, Crossref, OpenAlex, PubMed Central (PMC), CORE, Europe PMC, dblp, OpenAIRE, CiteSeerX, DOAJ, BASE, Zenodo, HAL, SSRN, Unpaywall (DOI lookup), and optional Sci-Hub workflows.

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

• Free-First Design: Open and public sources are prioritized before any optional commercial or restricted integrations.

• Optional API-Key Enhancement: Sources like Semantic Scholar can work better with a user-provided API key, but are not intended to force paid usage.

• Discovery + Retrieval Workflow: Google Scholar and Crossref can be used for discovery and DOI backfilling, while open repositories and publisher links are used for lawful full-text resolution where available.

• OA-First Fallback Chain: download_with_fallback now follows source-native download → OpenAIRE/CORE/Europe PMC/PMC discovery → Unpaywall DOI resolution → optional Sci-Hub.

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

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

Source Strategy

The long-term goal is not to depend on a single search engine, but to combine multiple free and public sources with clear roles:

• Open metadata backbone: Crossref, OpenAlex, Semantic Scholar, dblp, CiteSeerX, SSRN, Unpaywall (DOI-centric OA metadata).

• Discipline-specific sources: arXiv, PubMed, PubMed Central, Europe PMC, IACR.

• Open-access full-text sources: arXiv, PMC, CORE, OpenAIRE, DOAJ, BASE, Zenodo, HAL, publisher open-access links.

• Discovery and DOI recovery: Google Scholar can be useful for finding titles, versions, and DOI clues when other public metadata sources are incomplete.

Recommended free-first roadmap:

1. Keep current public sources stable.

2. Add OpenAlex as a broad free metadata source.

3. Add PubMed Central and Europe PMC for stronger biomedical full-text access.

4. Add CORE and OpenAIRE for repository-based open-access retrieval.

5. Use Google Scholar mainly as a discovery fallback, not as the primary canonical source.

Platform Capability Matrix

This matrix reflects verified live-integration results from functional and end-to-end regression tests in this repository. Columns show the highest capability level observed under normal conditions.

✅ = reliable in live tests. ⚠️ = works but subject to upstream instability or access restrictions. ❌ = not supported. 🔑 = key required. 🚧 = skeleton only.

Credential & API Key Requirements

All keys are optional unless noted. Configure them in .env (preferred) or as shell exports.

All variables follow the PAPER_SEARCH_MCP_<NAME> prefix scheme. Legacy names without the prefix (e.g. CORE_API_KEY, UNPAYWALL_EMAIL) are still supported for backward compatibility.

Known Upstream Limitations

Some search failures are caused by external provider instability, not by bugs in this project:

Optional Paid Platform Connectors (Phase 3)

IEEE Xplore and ACM Digital Library connectors are included as opt-in skeletons. They are disabled by default — no API calls are made unless you explicitly configure the corresponding keys.

How to enable:

export PAPER_SEARCH_MCP_IEEE_API_KEY=<your_ieee_key>       # free key at https://developer.ieee.org/
export PAPER_SEARCH_MCP_ACM_API_KEY=<your_acm_key>         # see https://libraries.acm.org/digital-library

Once a key is set, the corresponding source is automatically added to ALL_SOURCES and its MCP tools (search_ieee / search_acm, download_ieee / download_acm, read_ieee_paper / read_acm_paper) are registered at server startup.

Without a key the connectors log a startup warning only — the rest of the server is unaffected.

Free Source Expansion (Phase 4)

Three additional free-source connectors are now integrated into the MCP server:

• zenodo: Official Zenodo REST API connector (search + record-dependent PDF/read support).

• hal: HAL public API connector (search + record-dependent PDF/read support).

• ssrn: Discovery-first connector with hardened parser and best-effort download/read when a direct public PDF link is available.

• unpaywall: DOI-centric OA metadata source for standalone lookup (search_unpaywall) and fallback URL resolution.

SSRN integration remains compliance-first: it only attempts direct public PDF links exposed by SSRN pages. If login/restricted delivery is required, the connector returns a clear message instead of bypassing access controls.

Sci-Hub Notice

Sci-Hub support can remain available as an optional connector for users who explicitly choose to enable it, but it should not be treated as the default or recommended full-text path.

• Availability is unstable and mirrors change frequently.

• Legal and policy risks vary by jurisdiction.

• README and tool descriptions should clearly state that users are responsible for enabling and using it.

• Open-access and publisher-permitted sources should be tried first whenever possible.

Installation

Choose the method that best fits your workflow. All methods support the same optional API keys.

Config file locations

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

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

• Linux: ~/.config/Claude/claude_desktop_config.json

Method 1 — Smithery (one-command, recommended for Claude Desktop)

npx -y @smithery/cli install @openags/paper-search-mcp --client claude

Smithery automatically writes the correct config block for you. No manual JSON editing needed.

Method 2 — uvx (no install, always latest)

uvx runs the package directly from PyPI without a permanent install. Requires uv.

# Install uv (skip if already installed)
curl -LsSf https://astral.sh/uv/install.sh | sh

⚠️ macOS note: uvx generated wrapper scripts rely on realpath, which is not included in macOS by default. If you see a realpath: command not found error, either install GNU coreutils (brew install coreutils) or use Method 3 ( instead — it does not have this limitation.

Claude Desktop config:

{
  "mcpServers": {
    "paper-search-mcp": {
      "command": "uvx",
      "args": ["paper-search-mcp"],
      "env": {
        "PAPER_SEARCH_MCP_UNPAYWALL_EMAIL": "your@email.com",
        "PAPER_SEARCH_MCP_CORE_API_KEY": "",
        "PAPER_SEARCH_MCP_SEMANTIC_SCHOLAR_API_KEY": "",
        "PAPER_SEARCH_MCP_ZENODO_ACCESS_TOKEN": "",
        "PAPER_SEARCH_MCP_GOOGLE_SCHOLAR_PROXY_URL": "",
        "PAPER_SEARCH_MCP_IEEE_API_KEY": "",
        "PAPER_SEARCH_MCP_ACM_API_KEY": ""
      }
    }
  }
}

Method 3 — uv (persistent install)

uv tool install paper-search-mcp

Claude Desktop config:

{
  "mcpServers": {
    "paper-search-mcp": {
      "command": "uv",
      "args": ["tool", "run", "paper-search-mcp"],
      "env": {
        "PAPER_SEARCH_MCP_UNPAYWALL_EMAIL": "your@email.com",
        "PAPER_SEARCH_MCP_CORE_API_KEY": "",
        "PAPER_SEARCH_MCP_SEMANTIC_SCHOLAR_API_KEY": "",
        "PAPER_SEARCH_MCP_ZENODO_ACCESS_TOKEN": "",
        "PAPER_SEARCH_MCP_GOOGLE_SCHOLAR_PROXY_URL": "",
        "PAPER_SEARCH_MCP_IEEE_API_KEY": "",
        "PAPER_SEARCH_MCP_ACM_API_KEY": ""
      }
    }
  }
}

Method 4 — pip (standard Python install)

pip install paper-search-mcp

Claude Desktop config:

{
  "mcpServers": {
    "paper-search-mcp": {
      "command": "python",
      "args": ["-m", "paper_search_mcp.server"],
      "env": {
        "PAPER_SEARCH_MCP_UNPAYWALL_EMAIL": "your@email.com",
        "PAPER_SEARCH_MCP_CORE_API_KEY": "",
        "PAPER_SEARCH_MCP_SEMANTIC_SCHOLAR_API_KEY": "",
        "PAPER_SEARCH_MCP_ZENODO_ACCESS_TOKEN": "",
        "PAPER_SEARCH_MCP_GOOGLE_SCHOLAR_PROXY_URL": "",
        "PAPER_SEARCH_MCP_IEEE_API_KEY": "",
        "PAPER_SEARCH_MCP_ACM_API_KEY": ""
      }
    }
  }
}

If python is not on your PATH, replace it with the full path (e.g. /usr/bin/python3 or C:\Python311\python.exe). Run which python3 / where python to find it.

Method 5 — npx (via Smithery CLI, no local Python needed)

npx -y @smithery/cli run @openags/paper-search-mcp

Claude Desktop config:

{
  "mcpServers": {
    "paper-search-mcp": {
      "command": "npx",
      "args": ["-y", "@smithery/cli", "run", "@openags/paper-search-mcp"],
      "env": {
        "PAPER_SEARCH_MCP_UNPAYWALL_EMAIL": "your@email.com",
        "PAPER_SEARCH_MCP_CORE_API_KEY": "",
        "PAPER_SEARCH_MCP_SEMANTIC_SCHOLAR_API_KEY": ""
      }
    }
  }
}

Method 6 — Docker

docker build -t paper-search-mcp .
docker run --rm -i \
  -e PAPER_SEARCH_MCP_UNPAYWALL_EMAIL=your@email.com \
  -e PAPER_SEARCH_MCP_CORE_API_KEY=your_core_key \
  paper-search-mcp

Claude Desktop config:

{
  "mcpServers": {
    "paper-search-mcp": {
      "command": "docker",
      "args": ["run", "--rm", "-i", "paper-search-mcp"],
      "env": {
        "PAPER_SEARCH_MCP_UNPAYWALL_EMAIL": "your@email.com",
        "PAPER_SEARCH_MCP_CORE_API_KEY": "",
        "PAPER_SEARCH_MCP_SEMANTIC_SCHOLAR_API_KEY": "",
        "PAPER_SEARCH_MCP_ZENODO_ACCESS_TOKEN": "",
        "PAPER_SEARCH_MCP_GOOGLE_SCHOLAR_PROXY_URL": "",
        "PAPER_SEARCH_MCP_IEEE_API_KEY": "",
        "PAPER_SEARCH_MCP_ACM_API_KEY": ""
      }
    }
  }
}

Method 7 — Clone & run from source (development / recommended for macOS local)

This is the most reliable method on macOS — no wrapper scripts, no realpath issues.

# 1. Install uv (skip if already installed)
curl -LsSf https://astral.sh/uv/install.sh | sh

# 2. Clone repo
git clone https://github.com/openags/paper-search-mcp.git
cd paper-search-mcp

# 3. Verify it runs (uv auto-resolves dependencies, no manual install needed)
uv run -m paper_search_mcp.server

Claude Desktop config (replace the directory path with your actual clone location):

{
  "mcpServers": {
    "paper-search-mcp": {
      "command": "uv",
      "args": [
        "run",
        "--directory", "/path/to/paper-search-mcp",
        "-m", "paper_search_mcp.server"
      ],
      "env": {
        "PAPER_SEARCH_MCP_UNPAYWALL_EMAIL": "your@email.com",
        "PAPER_SEARCH_MCP_CORE_API_KEY": "",
        "PAPER_SEARCH_MCP_SEMANTIC_SCHOLAR_API_KEY": "",
        "PAPER_SEARCH_MCP_ZENODO_ACCESS_TOKEN": "",
        "PAPER_SEARCH_MCP_GOOGLE_SCHOLAR_PROXY_URL": "",
        "PAPER_SEARCH_MCP_IEEE_API_KEY": "",
        "PAPER_SEARCH_MCP_ACM_API_KEY": ""
      }
    }
  }
}

For example, if you cloned to /Users/mac/Pengsong/paper-search-mcp:

"args": ["run", "--directory", "/Users/mac/Pengsong/paper-search-mcp", "-m", "paper_search_mcp.server"]

uv run automatically installs dependencies into an isolated environment on first run — no pip install or venv needed.

For active development, optionally install an editable copy:

uv venv && source .venv/bin/activate   # Windows: .venv\Scripts\activate
uv pip install -e ".[dev]"

Environment Variables (.env file)

Instead of putting keys directly in the JSON config you can store them in a .env file in the project root (auto-loaded on startup):

cp .env.example .env   # if running from source
# or create ~/.paper-search-mcp.env for global use

PAPER_SEARCH_MCP_UNPAYWALL_EMAIL=your@email.com
PAPER_SEARCH_MCP_CORE_API_KEY=
PAPER_SEARCH_MCP_SEMANTIC_SCHOLAR_API_KEY=
PAPER_SEARCH_MCP_ZENODO_ACCESS_TOKEN=
PAPER_SEARCH_MCP_GOOGLE_SCHOLAR_PROXY_URL=
PAPER_SEARCH_MCP_IEEE_API_KEY=
PAPER_SEARCH_MCP_ACM_API_KEY=

To use a custom path: export PAPER_SEARCH_MCP_ENV_FILE=/absolute/path/to/.env

Legacy variable names without the PAPER_SEARCH_MCP_ prefix (e.g. CORE_API_KEY, UNPAYWALL_EMAIL) are still supported for backward compatibility.

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/paper-search-mcp.git
   cd paper-search-mcp
   uv venv && source .venv/bin/activate
   uv pip install -e ".[dev]"

3. Make Changes:

   • Add new platforms in academic_platforms/.

   • Update tests in tests/.

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

Demo

TODO

Planned Academic Platforms

• [√] arXiv

• [√] PubMed

• [√] bioRxiv

• [√] medRxiv

• [√] Google Scholar

• [√] IACR ePrint Archive

• [√] Semantic Scholar

• [√] Crossref

• [√] PubMed Central (PMC)

• [√] CORE

• [√] Europe PMC

• [√] Sci-Hub warning and enablement docs

Development Tasks

• [√] Fix Async search bugs and ensure reliable fast MCP events

• [√] End-to-End full pipeline testing script (search, parse, download)

• [√] Establish two-layer federated architecture (Layer 1 tool: search_papers)

• [√] Ensure pervasive DOI extraction across metadata fields & abstract fallbacks

• Citation graph & Paper relation context feature

• [√] Expand full-stack OpenAlex provider

Priority Free and Open Sources

• [√] PubMed Central (PMC)

• [√] CORE

• [√] OpenAlex

• [√] Europe PMC

• [√] OpenAIRE

• [√] dblp

• [√] CiteSeerX

• [√] DOAJ

• [√] BASE

• [√] Zenodo

• [√] HAL

• [√] SSRN (discovery + best-effort full-text)

• [√] Unpaywall (standalone DOI search source)

Optional and Non-Core Integrations

• ResearchGate

• JSTOR

• ScienceDirect

• Springer Link

• [√] IEEE Xplore (optional skeleton — activate with IEEE_API_KEY)

• [√] ACM Digital Library (optional skeleton — activate with ACM_API_KEY)

• Web of Science

• Scopus

Star History

License

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

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