Context Optimizer MCP Server

Context Optimizer MCP Server

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.

Category
Visit Server

README

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.

Related Projects

Recommended Servers

playwright-mcp

playwright-mcp

A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.

Official
Featured
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.

Official
Featured
Local
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.

Official
Featured
Local
TypeScript
VeyraX MCP

VeyraX MCP

Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.

Official
Featured
Local
graphlit-mcp-server

graphlit-mcp-server

The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.

Official
Featured
TypeScript
Kagi MCP Server

Kagi MCP Server

An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.

Official
Featured
Python
E2B

E2B

Using MCP to run code via e2b.

Official
Featured
Neon Database

Neon Database

MCP server for interacting with Neon Management API and databases

Official
Featured
Exa Search

Exa Search

A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.

Official
Featured
Qdrant Server

Qdrant Server

This repository is an example of how to create a MCP server for Qdrant, a vector search engine.

Official
Featured