Enhances GitHub Copilot in VS Code with CodeAlive's deep code understanding, allowing it to leverage semantic search and project-wide context for improved code suggestions.
Supports direct invocation via Python interpreter as an alternative configuration option for running the MCP server with AI clients.
This MCP (Model Context Protocol) server for the CodeAlive API enables AI clients like Claude Desktop, Cursor, Windserf, VS Code (GitHub Copilot), Cline, Roo-Code, and Refact to access CodeAlive's advanced semantic code search and codebase interaction features.
CodeAlive MCP enhances these agents by providing enriched context from your project's codebase, enabling more intelligent and efficient interactions.
CodeAlive is a platform that analyzes your entire codebase, including documentation and dependencies, to understand its structure, patterns, and logic. It creates a detailed internal map of your repositories or workspaces, enabling fast, reliable, and high-quality answers to questions about your solution for any IT professional.
Using this MCP server allows AI agents (like Claude, Copilot, etc.) to leverage CodeAlive's deep code understanding. This helps agents:
The MCP server provides the following tools:
chat_completions: Access the CodeAlive Chat API with codebase context. If your API key is assigned to exactly one datasource, specifying the datasource is optional.get_data_sources: List available repositories and workspaces indexed by CodeAlive.search_code: Search for code snippets across your datasources using CodeAlive's semantic search. If your API key is assigned to exactly one datasource, specifying the datasource is optional.Configure the server using environment variables or command-line arguments.
The following environment variables are supported:
CODEALIVE_API_KEY: Your CodeAlive API key. (Required unless passed via --api-key)--api-key: Your CodeAlive API key. Overrides the CODEALIVE_API_KEY environment variable.--transport: Transport type: "stdio" (default) or "sse".--host: Host address for SSE transport (default: 0.0.0.0).--port: Port for SSE transport (default: 8000).--debug: Enable debug mode with verbose logging to standard output/error.Below are configuration examples for popular AI clients. Remember to replace placeholders like /path/to/your/codealive-mcp and YOUR_API_KEY_HERE with your actual values. Using environment variables (env block) is generally recommended over putting the API key directly in the configuration file.
.continue/config.yaml or globally in ~/.continue/config.yaml:~/Library/Application Support/Claude/claude_desktop_config.json%APPDATA%\Claude\claude_desktop_config.json (usually C:\Users\YourUsername\AppData\Roaming\Claude\claude_desktop_config.json)Ctrl+Shift+P or Cmd+Shift+P) and selecting "Preferences: Open User Settings (JSON)".settings.json:Cmd+, or Ctrl+,).If you prefer not to use uv, you can invoke the server script directly using the Python interpreter from your virtual environment. Update the command and args in the client configurations accordingly.
If the MCP server isn't working correctly with your AI client, follow these steps:
--debug flag to the args in your client's MCP configuration. This will print verbose logs from the MCP server itself to its standard output/error stream. Where this stream goes depends on how the client manages the MCP process.~/Library/Logs/Claude/mcp.log and ~/Library/Logs/Claude/mcp-server-codealive.log%LOCALAPPDATA%\Claude\Logs\mcp.log and %LOCALAPPDATA%\Claude\Logs\mcp-server-codealive.log (Path is typically C:\Users\YourUsername\AppData\Local\Claude\Logs)Cmd+Shift+P / Ctrl+Shift+P) -> Developer: Toggle Developer Tools -> Console tab (for browser-level errors).View -> Output (or click Output in the bottom panel). In the dropdown menu on the right side of the Output panel, look for a channel named CodeAlive, MCP, or related to the server process. This often contains the direct stdout/stderr from the MCP server if --debug is enabled.Developer: Open Logs Folder. Check files within, especially related to the main process or extension host.~/Library/Application Support/Cursor/logs/%APPDATA%\Cursor\logs\ (Typically C:\Users\YourUsername\AppData\Roaming\Cursor\logs\)Cmd+Shift+P / Ctrl+Shift+P) -> Developer: Toggle Developer Tools -> Console tab (for browser-level errors).View -> Output (or click Output in the bottom panel). In the dropdown menu on the right side of the Output panel, look for a channel named CodeAlive, MCP, GitHub Copilot, or Continue. The MCP server logs (especially with --debug) might be routed here.Developer: Show Logs... -> Select Extension Host from the dropdown. Look for errors related to Copilot or Continue extensions trying to communicate via MCP.Continue: Focus on Continue Console View (requires enabling Continue: Enable Console in settings). See Continue Troubleshooting Docs.command, args, and env paths and values in your client's MCP configuration file. Ensure JSON/YAML syntax is correct.CODEALIVE_API_KEY is correct.If problems persist, consider opening an issue on the CodeAlive MCP server repository (if available) with relevant logs and configuration details (masking your API key).
You can also contact our support team at support@codealive.dev for further assistance.
This project is licensed under the MIT License - see the LICENSE file for details.
This server cannot be installed
local-only server
The server can only run on the client's local machine because it depends on local resources.
A Model Context Protocol server that enhances AI agents by providing deep semantic understanding of codebases, enabling more intelligent interactions through advanced code search and contextual awareness.