context-ai-mcp-server

Steliosgeox/context-ai-mcp-server

3.1

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The Context AI MCP Server is a comprehensive Model Context Protocol server designed to enhance AI capabilities by providing rich context from your workspace.

Tools
5
Resources
0
Prompts
0

Context AI MCP Server

A comprehensive Model Context Protocol (MCP) server that enhances AI capabilities by providing rich context from your workspace. This server is designed to make AI assistants smarter by giving them deep understanding of your codebase, patterns, and project structure.

🚀 Features

🔍 Workspace Analysis

  • Complete Code Analysis: Analyzes entire workspace structure, technologies, and patterns
  • Dependency Mapping: Tracks file dependencies and relationships
  • Technology Detection: Automatically identifies frameworks, libraries, and tools
  • Architecture Insights: Recognizes architectural patterns and design principles

🛠️ Powerful Tools

  • analyze_workspace: Comprehensive workspace analysis with customizable depth
  • search_codebase: Advanced code search with regex support and context
  • get_file_dependencies: Detailed dependency analysis for any file
  • get_project_patterns: Extract architectural patterns and conventions
  • get_context_summary: Generate AI-optimized workspace summaries
  • suggest_improvements: Automated code quality and security recommendations

📝 Smart Prompts

  • Code Review Templates: Comprehensive code review checklists
  • Architecture Analysis: System design evaluation prompts
  • Security Audits: Security-focused analysis templates
  • Performance Optimization: Performance analysis and improvement guides
  • Refactoring Suggestions: Code quality improvement templates

📁 Resource Access

  • Direct access to all workspace files
  • Intelligent file filtering and organization
  • MIME type detection and proper content handling
  • Efficient caching for better performance

🎯 Use Cases

For Development Teams

  • Onboarding: Help new team members understand codebase structure
  • Code Reviews: Automated analysis and comprehensive review checklists
  • Architecture Planning: Analyze current patterns and plan improvements
  • Knowledge Transfer: Preserve and share architectural knowledge

For AI Enhancement

  • Context Enrichment: Provide comprehensive workspace context to AI assistants
  • Pattern Recognition: Help AI understand your specific coding patterns
  • Smart Suggestions: Get AI recommendations based on your actual codebase
  • Automated Analysis: Regular codebase health checks and improvement suggestions

🚦 Getting Started

Prerequisites

  • Node.js 18+
  • TypeScript
  • VS Code or compatible MCP client

Installation

  1. Clone or create the project:

    git clone <your-repo> context-ai-mcp-server
cd context-ai-mcp-server

  2. Install dependencies:

    npm install

  3. Build the project:

    npm run build

  4. Configure your MCP client:

    For VS Code: The .vscode/mcp.json file is already configured. Just open VS Code and the server will be available.

    For Claude Desktop: Add to your claude_desktop_config.json:

    {
  "mcpServers": {
    "context-ai-mcp-server": {
      "command": "node",
      "args": [
        "/ABSOLUTE/PATH/TO/context-ai-mcp-server/dist/index.js"
      ],
      "env": {
        "WORKSPACE_PATH": "/ABSOLUTE/PATH/TO/YOUR/PROJECT"
      }
    }
  }
}

Usage Examples

  1. Analyze Your Workspace:

    Use the analyze_workspace tool to get comprehensive insights about your project structure, technologies, and patterns.

  2. Search Your Code:

    Use search_codebase with queries like "function.*authenticate" to find authentication-related code across your project.

  3. Get Context Summary:

    Use get_context_summary to generate AI-optimized summaries of your workspace for enhanced AI interactions.

🔧 Configuration

Environment Variables

  • WORKSPACE_PATH: Path to the workspace to analyze (defaults to current directory)

Customization

The server can be customized by modifying:

  • Prompt Templates: Edit src/prompt-templates.ts to add custom prompts
  • Analysis Logic: Modify src/context-manager.ts for custom analysis patterns
  • File Indexing: Adjust src/workspace-indexer.ts for different file handling

🏗️ Architecture

├── src/
│   ├── index.ts              # Main MCP server implementation
│   ├── workspace-indexer.ts  # File system analysis and indexing
│   ├── context-manager.ts    # Context extraction and management
│   └── prompt-templates.ts   # Predefined prompt templates
├── dist/                     # Compiled JavaScript output
├── .vscode/
│   └── mcp.json             # VS Code MCP configuration
└── .github/
    └── copilot-instructions.md # GitHub Copilot customization

🚀 Development

Scripts

  • npm run build: Compile TypeScript to JavaScript
  • npm run dev: Watch mode for development
  • npm start: Run the compiled server

Adding New Tools

  1. Add tool definition in src/index.ts setupToolHandlers()
  2. Implement tool logic in src/context-manager.ts
  3. Add corresponding method in the main server class
  4. Update documentation

Adding New Prompts

  1. Add prompt template in src/prompt-templates.ts
  2. Implement prompt generation logic
  3. Test with MCP clients

🤝 Contributing

  1. Fork the repository
  2. Create a feature branch
  3. Make your changes
  4. Add tests if applicable
  5. Submit a pull request

📄 License

MIT License - see LICENSE file for details

🔗 Links

Made with ❤️ to enhance AI capabilities through rich workspace context