MCP implementation of Claude Code capabilities enabling code understanding, modification, execution and agent delegation via the Model Context Protocol.
https://github.com/SDGLBL/mcp-claude-codeStop being locked into Claude Desktop for your coding workflows. This MCP server brings Claude Code's powerful file manipulation, code understanding, and execution capabilities to any MCP-compatible client—giving you the flexibility to integrate these tools wherever you work.
Claude Code is incredibly useful, but it's trapped in Claude Desktop. You can't use it in your IDE, your custom applications, or other AI interfaces. This MCP server fixes that limitation by implementing the same core capabilities through the Model Context Protocol.
The result? You get intelligent code assistance that can:
All while integrating seamlessly into your existing development environment.
Faster Code Reviews: The grep_ast tool shows you function and class context around matches, not just line numbers. When reviewing a large codebase, you instantly see where functions are defined and how they're structured.
Surgical File Edits: The multi_edit tool lets you make multiple precise replacements in a single atomic operation. No more manually tracking line numbers or worrying about edits interfering with each other.
Concurrent Task Execution: The dispatch_agent tool launches multiple specialized agents that work simultaneously. While one agent analyzes your database schema, another can be documenting your API endpoints and a third can be running tests.
Multi-Provider Flexibility: Unlike being locked to Anthropic's models, you can configure any LiteLLM-compatible provider. Use GPT-4 for code generation, Claude for analysis, or switch providers based on the task.
Security Without Friction: Permission prompts for file modifications, directory restrictions, and input sanitization—but streamlined enough that you won't disable it.
Performance-Optimized Search: Uses ripgrep under the hood for blazing-fast codebase searches. The grep tool can scan massive repositories in milliseconds.
Notebook-Native: Full Jupyter support with cell-level editing, not just file-level operations. Edit specific cells, insert new ones, or extract code while preserving outputs.
Shell Integration: The run_command tool handles shell operations intelligently, supporting complex command chains and proper error handling.
IDE Extensions: Build VS Code or JetBrains plugins that offer Claude Code functionality directly in your editor.
CI/CD Pipelines: Integrate intelligent code analysis and modification into your build processes.
Code Review Automation: Deploy as a service that analyzes pull requests and suggests improvements.
Documentation Generation: Use the agent delegation to automatically generate and maintain technical documentation across your codebase.
Jupyter Workflows: Seamlessly integrate with data science workflows, editing notebooks programmatically while preserving execution state.
The setup is straightforward—Python 3.9+, install dependencies, configure your LLM providers, and run the server. The included tutorial walks through practical examples of each tool.
git clone https://github.com/SDGLBL/mcp-claude-code.git
cd mcp-claude-code
pip install -r requirements.txt
python -m mcp_claude_code.server --host 0.0.0.0 --port 8000
Configure your preferred LLM providers in config.yaml and you're ready to integrate Claude Code capabilities into any MCP client.
This isn't about replacing your existing workflow—it's about extending it with the intelligent code assistance you need, where you need it.