Aditya Karnam/mcp-scholarly
Built by Metorial, the integration platform for agentic AI.
Aditya Karnam/mcp-scholarly
Server Summary
Search academic articles
Access arXiv database
Retrieve scholarly information
A MCP server to search for accurate academic articles. More scholarly vendors will be added soon.
The server implements one tool:
On MacOS: ~/Library/Application\ Support/Claude/claude_desktop_config.json
On Windows: %APPDATA%/Claude/claude_desktop_config.json
Development/Unpublished Servers Configuration
"mcpServers": {
"mcp-scholarly": {
"command": "uv",
"args": [
"--directory",
"/Users/adityakarnam/PycharmProjects/mcp-scholarly/mcp-scholarly",
"run",
"mcp-scholarly"
]
}
}
Published Servers Configuration
"mcpServers": {
"mcp-scholarly": {
"command": "uvx",
"args": [
"mcp-scholarly"
]
}
}
or if you are using Docker
Published Docker Servers Configuration
"mcpServers": {
"mcp-scholarly": {
"command": "docker",
"args": [
"run", "--rm", "-i",
"mcp/scholarly"
]
}
}
To install mcp-scholarly for Claude Desktop automatically via Smithery:
npx -y @smithery/cli install mcp-scholarly --client claude
To prepare the package for distribution:
uv sync
uv build
This will create source and wheel distributions in the dist/
directory.
uv publish
Note: You'll need to set PyPI credentials via environment variables or command flags:
--token
or UV_PUBLISH_TOKEN
--username
/UV_PUBLISH_USERNAME
and --password
/UV_PUBLISH_PASSWORD
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.