MySQL Server MCP Server

# mcp-scholarly MCP server [![smithery badge](https://smithery.ai/badge/mcp-scholarly)](https://smithery.ai/server/mcp-scholarly) A MCP server to search for accurate academic articles. More scholarly vendors will be added soon. ![demo1.jpeg](examples/demo1.png) ![image](https://github.com/user-attachments/assets/13202184-bc12-4530-b7c1-2ee698f3e1cc) <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Development/Unpublished Servers Configuration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](https://smithery.ai/server/mcp-scholarly): ```bash npx -y @smithery/cli install mcp-scholarly --client claude ``` ## Development ### Building and Publishing To prepare the package for distribution: 1. Sync dependencies and update lockfile: ```bash uv sync ``` 2. Build package distributions: ```bash uv build ``` This will create source and wheel distributions in the `dist/` directory. 3. Publish to PyPI: ```bash uv publish ``` Note: You'll need to set PyPI credentials via environment variables or command flags: - Token: `--token` or `UV_PUBLISH_TOKEN` - Or username/password: `--username`/`UV_PUBLISH_USERNAME` and `--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](https://github.com/modelcontextprotocol/inspector). You can launch the MCP Inspector via [`npm`](https://docs.npmjs.com/downloading-and-installing-node-js-and-npm) with this command: ```bash 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.