Context Optimizer MCP Server

npm version license node tests

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. It enables AI assistants to extract targeted information rather than processing large files and command outputs in their entirety.

This server provides context optimization functionality similar to the VS Code Copilot Context Optimizer extension, but with compatibility across MCP-supporting applications.

Features

🔍 File Analysis Tool (askAboutFile) - Extract specific information from files without loading entire contents
🖥️ Terminal Execution Tool (runAndExtract) - Execute commands and extract relevant information using LLM analysis
❓ Follow-up Questions Tool (askFollowUp) - Continue conversations about previous terminal executions
🔬 Research Tools (researchTopic, deepResearch) - Conduct web research using Exa.ai's API
🔒 Security Controls - Path validation, command filtering, and session management
🔧 Multi-LLM Support - Works with Google Gemini, Claude (Anthropic), and OpenAI
⚙️ Environment Variable Configuration - API key management through system environment variables
🏗️ Simple Configuration - Environment variables only, no config files to manage
🧪 Comprehensive Testing - Unit tests, integration tests, and security validation

Quick Start

1. Install globally:

npm install -g context-optimizer-mcp-server

2. Set environment variables (see docs/guides/usage.md for OS-specific instructions):

export CONTEXT_OPT_LLM_PROVIDER="gemini"
export CONTEXT_OPT_GEMINI_KEY="your-gemini-api-key"
export CONTEXT_OPT_EXA_KEY="your-exa-api-key"
export CONTEXT_OPT_ALLOWED_PATHS="/path/to/your/projects"

3. Add to your MCP client configuration:

For Claude Desktop (claude_desktop_config.json):

{
  "mcpServers": {
    "context-optimizer": {
      "command": "context-optimizer-mcp"
    }
  }
}

For VS Code (mcp.json):

{
  "servers": {
    "context-optimizer": {
      "command": "context-optimizer-mcp"
    }
  }
}

For complete setup instructions including OS-specific environment variable configuration and AI assistant setup, see docs/guides/usage.md.

Available Tools

askAboutFile - Extract specific information from files without loading entire contents into chat context. Perfect for checking if files contain specific functions, extracting import/export statements, or understanding file purpose without reading the full content.
runAndExtract - Execute terminal commands and intelligently extract relevant information using LLM analysis. Supports non-interactive commands with security validation, timeouts, and session management for follow-up questions.
askFollowUp - Continue conversations about previous terminal executions without re-running commands. Access complete context from previous runAndExtract calls including full command output and execution details.
researchTopic - Conduct quick, focused web research on software development topics using Exa.ai's research capabilities. Get current best practices, implementation guidance, and up-to-date information on evolving technologies.
deepResearch - Comprehensive research and analysis using Exa.ai's exhaustive capabilities for critical decision-making and complex architectural planning. Ideal for strategic technology decisions, architecture planning, and long-term roadmap development.

For detailed tool documentation and examples, see docs/tools.md and docs/guides/usage.md.

Documentation

All documentation is organized under the docs/ directory:

Topic	Location	Description
Architecture	`docs/architecture.md`	System design and component overview
Tools Reference	`docs/tools.md`	Complete tool documentation and examples
Usage Guide	`docs/guides/usage.md`	Complete setup and configuration
VS Code Setup	`docs/guides/vs-code-setup.md`	VS Code specific configuration
Troubleshooting	`docs/guides/troubleshooting.md`	Common issues and solutions
API Keys	`docs/reference/api-keys.md`	API key management
Testing	`docs/reference/testing.md`	Testing framework and procedures
Changelog	`docs/reference/changelog.md`	Version history
Contributing	`docs/reference/contributing.md`	Development guidelines
Security	`docs/reference/security.md`	Security policy
Code of Conduct	`docs/reference/code-of-conduct.md`	Community guidelines

Quick Links

Get Started: See docs/guides/usage.md for complete setup instructions
Tools Reference: Check docs/tools.md for detailed tool documentation
Troubleshooting: Check docs/guides/troubleshooting.md for common issues
VS Code Setup: Follow docs/guides/vs-code-setup.md for VS Code configuration

Testing

# Run all tests (skips LLM integration tests without API keys)
npm test

# Run tests with API keys for full integration testing
# Set environment variables first:
export CONTEXT_OPT_LLM_PROVIDER="gemini"
export CONTEXT_OPT_GEMINI_KEY="your-gemini-key"
export CONTEXT_OPT_EXA_KEY="your-exa-key"
npm test  # Now runs all tests including LLM integration

# Run in watch mode
npm run test:watch

For detailed testing setup, see docs/reference/testing.md.

Contributing

Contributions are welcome! Please read docs/reference/contributing.md for guidelines on development workflow, coding standards, testing, and submitting pull requests.

Community

Code of Conduct: See docs/reference/code-of-conduct.md
Security Reports: Follow docs/reference/security.md for responsible disclosure
Issues: Use GitHub Issues for bugs & feature requests
Pull Requests: Ensure tests pass and docs are updated
Discussions: (If enabled) Use for open-ended questions/ideas

License

MIT License - see LICENSE file for details.

VS Code Copilot Context Optimizer – Original VS Code extension (companion project)

This server cannot be installed

security - not tested

license - permissive license

quality - not tested

How are these scores calculated?

hybrid server

The server is able to function both locally and remotely, depending on the configuration or use case.

Provides AI coding assistants with context optimization tools including targeted file analysis, intelligent terminal command execution with LLM-powered output extraction, and web research capabilities. Helps reduce token usage by extracting only relevant information instead of processing entire files and command outputs.

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