A Model Context Protocol (MCP) server that provides context optimization tools for AI coding assistants including GitHub Copilot, Cursor AI, Claude Desktop, and other MCP-compatible assistants enabling them to extract targeted information rather than processing large terminal outputs and files wasting their context.
git clone https://github.com/malaksedarous/context-optimizer-mcp-server.gitA Model Context Protocol (MCP) server that provides context optimization tools for AI coding assistants including GitHub Copilot, Cursor AI, Claude Desktop, and other MCP-compatible assistants enabling them to extract targeted information rather than processing large terminal outputs and files wasting their context.
No install command available. Check the GitHub repository for manual installation instructions.
git clone https://github.com/malaksedarous/context-optimizer-mcp-serverCopy the install command above and run it in your terminal.
Launch Claude Code, Cursor, or your preferred AI coding agent.
Use the prompt template or examples below to test the skill.
Adapt the skill to your specific use case and workflow.
Set up a context-optimizer-mcp-server for [COMPANY]'s [INDUSTRY] development team. The server should optimize context for AI coding assistants like GitHub Copilot and Cursor AI. Provide step-by-step instructions for installation, configuration, and integration with the team's existing workflows.
## Context Optimizer MCP Server Setup Guide ### Step 1: Installation 1. Clone the repository: `git clone https://github.com/[COMPANY]/context-optimizer-mcp-server.git` 2. Navigate to the project directory: `cd context-optimizer-mcp-server` 3. Install dependencies: `npm install` 4. Build the project: `npm run build` ### Step 2: Configuration 1. Create a `.env` file in the root directory. 2. Add the following environment variables: - `MCP_SERVER_PORT=3000` - `GITHUB_TOKEN=[YOUR_GITHUB_TOKEN]` - `CURSOR_API_KEY=[YOUR_CURSOR_API_KEY]` ### Step 3: Integration 1. Start the server: `npm start` 2. Integrate with GitHub Copilot and Cursor AI by adding the server URL to their settings. 3. Test the integration by running a sample command in your terminal.
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