MCP Scholarly Server
Enables users to search for academic articles on platforms like arXiv using specific keywords, with plans to integrate more scholarly databases in the future.
adityak74
Tools
search-arxiv
Search arxiv for articles related to the given keyword.
search-google-scholar
Search google scholar for articles related to the given keyword.
README
mcp-scholarly MCP server
A MCP server to search for accurate academic articles. More scholarly vendors will be added soon.

<a href="https://glama.ai/mcp/servers/aq05b2p0ql"><img width="380" height="200" src="https://glama.ai/mcp/servers/aq05b2p0ql/badge" alt="Scholarly Server MCP server" /></a>
Components
Tools
The server implements one tool:
- search-arxiv: Search arxiv for articles related to the given keyword.
- Takes "keyword" as required string arguments
Quickstart
Install
Claude Desktop
On MacOS: ~/Library/Application\ Support/Claude/claude_desktop_config.json
On Windows: %APPDATA%/Claude/claude_desktop_config.json
<details> <summary>Development/Unpublished Servers Configuration</summary>
"mcpServers": {
"mcp-scholarly": {
"command": "uv",
"args": [
"--directory",
"/Users/adityakarnam/PycharmProjects/mcp-scholarly/mcp-scholarly",
"run",
"mcp-scholarly"
]
}
}
</details>
<details> <summary>Published Servers Configuration</summary>
"mcpServers": {
"mcp-scholarly": {
"command": "uvx",
"args": [
"mcp-scholarly"
]
}
}
</details>
or if you are using Docker
<details> <summary>Published Docker Servers Configuration</summary>
"mcpServers": {
"mcp-scholarly": {
"command": "docker",
"args": [
"run", "--rm", "-i",
"mcp/scholarly"
]
}
}
</details>
Installing via Smithery
To install mcp-scholarly for Claude Desktop automatically via Smithery:
npx -y @smithery/cli install mcp-scholarly --client claude
Development
Building and Publishing
To prepare the package for distribution:
- Sync dependencies and update lockfile:
uv sync
- Build package distributions:
uv build
This will create source and wheel distributions in the dist/ directory.
- Publish to PyPI:
uv publish
Note: You'll need to set PyPI credentials via environment variables or command flags:
- Token:
--tokenorUV_PUBLISH_TOKEN - Or username/password:
--username/UV_PUBLISH_USERNAMEand--password/UV_PUBLISH_PASSWORD
Debugging
Since MCP servers run over stdio, debugging can be challenging. For the best debugging experience, we strongly recommend using the MCP Inspector.
You can launch the MCP Inspector via npm with this command:
npx @modelcontextprotocol/inspector uv --directory /Users/adityakarnam/PycharmProjects/mcp-scholarly/mcp-scholarly run mcp-scholarly
Upon launching, the Inspector will display a URL that you can access in your browser to begin debugging.
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