
Context Optimizer
Helps AI coding assistants extract only relevant information from files, terminal outputs, and web searches instead of consuming entire context with unnecessary data.
Provides targeted file content extraction, secure terminal command execution with intelligent output analysis, and web research capabilities while maintaining session state for follow-up interactions and enforcing security boundaries through path validation and command filtering.
What it does
- Extract targeted content from large files
- Execute terminal commands with filtered output analysis
- Perform focused web research and content extraction
- Maintain session state across interactions
- Filter command outputs to prevent context overflow
Best for
About Context Optimizer
Context Optimizer is a community-built MCP server published by malaksedarous that provides AI assistants with tools and capabilities via the Model Context Protocol. Context Optimizer offers web keyword analysis, website keyword analysis, and secure content extraction to help you find It is categorized under productivity, developer tools.
How to install
You can install Context Optimizer in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport.
License
Context Optimizer is released under the MIT license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.
Context Optimizer MCP Server
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.
This MCP server is the evolution of the VS Code Copilot Context Optimizer extension, but with compatibility across MCP-supporting applications.
π― The Problem It Solves
Have you ever experienced this with your AI coding assistant (like Copilot, Claude Code, or Cursor)?
- π Your assistant keeps compacting/summarizing conversations and losing a bit of the context in the process.
- π₯οΈ Terminal outputs flood the context with hundreds of lines when the assistant only needs key information.
- π Large files overwhelm the context when the assistant just needs to check one specific thing.
- β οΈ "Context limit reached" messages interrupting your workflow.
- π§ Your assistant "forgets" earlier parts of your conversation due to context overflow.
- π« The reasoning quality drops when you have a longer conversation.
The Root Cause: When your assistant:
- Reads long logs during builds, tests, lints, etc. after executing a terminal command.
- Reads a large file (or multiple) in full just to answer a question when it doesn't need the whole code.
- Reads multiple web pages from the web to search a topic to learn how to do something.
- Or just during a long conversation.
The assistant will either:
- Start compacting, summarizing or truncating the conversation history.
- Drop the quality of reasoning.
- Lose track of earlier context and decisions.
- Become less helpful as it loses focus.
The Solution:
This server provides any MCP-compatible assistant with specialized tools that extract only the specific information you need, keeping your chat context clean and focused on productive problem-solving rather than data management.
Features
- π File Analysis Tool (
askAboutFile) - Extract specific information from files without loading entire contents - π₯οΈ Terminal Execution Tool (
runAndExtract) - Execute commands and extract relevant information using LLM analysis - β Follow-up Questions Tool (
askFollowUp) - Continue conversations about previous terminal executions - π¬ Research Tools (
researchTopic,deepResearch) - Conduct web research using Exa.ai's API - π Security Controls - Path validation, command filtering, and session management
- π§ Multi-LLM Support - Works with Google Gemini, Claude (Anthropic), and OpenAI
- βοΈ Environment Variable Configuration - API key management through system environment variables
- ποΈ Simple Configuration - Environment variables only, no config files to manage
- π§ͺ 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:
like "mcpServers" in claude_desktop_config.json (Claude Desktop) or "servers" in mcp.json (VS Code).
"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 previousrunAndExtractcalls 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.mdfor complete setup instructions - Tools Reference: Check
docs/tools.mdfor detailed tool documentation - Troubleshooting: Check
docs/guides/troubleshooting.mdfor common issues - VS Code Setup: Follow
docs/guides/vs-code-setup.mdfor 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
Manual Testing
For comprehensive end-to-end testing with an AI assistant, see the Manual Testing Setup Guide. This provides a workflow-based testing protocol that validates all tools through realistic scenarios.
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
- VS Code Copilot Context Optimizer β Original VS Code extension (companion project)
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