This is a server that implements the MCP code execution mode. Through a single-tool bridge and zero-context discovery mechanism, it reduces the overhead of MCP tool calls from tens of thousands of tokens to about 200 tokens and securely executes Python code in the root container, supporting data science and secure isolation.

A RAG system based on Python code analysis. It parses the code structure through AST and stores it in the Weaviate vector database, providing code query, natural language Q&A, and visualization functions, and supporting multi-codebase management and dependency analysis.

A tool that exposes a secure local Python executor through the MCP protocol, providing code interpreter functionality for LLM applications

An MCP server for analyzing the structure, complexity, and dependencies of Python code.

CICADA is an MCP server that provides structured code indexing for AI code assistants. Through AST-level indexing, call site tracking, and semantic search, it provides efficient context compression for Elixir, Python, and Erlang code repositories, reducing token usage and improving the quality of code understanding.

An MCP server that provides remote pexpect session control, supporting programmatic interactive debugging with command-line debugging tools such as LLDB and GDB through Python code

Nash MCP is a multi - functional command execution server that supports Shell command execution, Python code execution, web content fetching, and task management, providing secure credential storage and session management functions.

The project involves documentation, sample code repositories, and community resources for the LangChain framework, including technical content such as Python programming, AI agent development, FastAPI integration, and LLM fine-tuning.

This project demonstrates how to debug the MCP server in VSCode, includes example code for Python and TypeScript, and integrates the MCP Inspector tool for browser testing.

CodeBox-AI is a secure Python code execution service that provides self-hosted code interpreter functionality, supports conversational code execution, dynamic package installation, and visual output, and integrates the MCP protocol for interaction with LLM applications.

A Python tool for converting CHM help files to Markdown format, supporting code highlighting, table preservation, and batch processing, facilitating version management of technical documents.

Fused MCP Agents is a Python - based tool that allows users to directly run any Python code through the Claude desktop application, enabling the connection between LLMs, APIs, and executable code.

A text editor server built on Python and FastMCP, providing powerful file operation tools. It enables file reading, editing, and management through a standardized API, and specifically optimizes the accuracy and reliability of code editing by AI assistants.

JEBMCP is a project that combines JEB and MCP tools, aiming to simplify reverse engineering tasks through Python scripts and provide efficient code analysis and operation functions.

An experimental local code indexing service based on Python that uses ChromaDB for storage and provides semantic search functionality, serving tools such as Cursor through the MCP protocol.

This project implements a MATLAB Model Context Protocol (MCP) server, supporting interaction with a shared MATLAB session through a Python interface. It provides functions such as code execution and variable retrieval, and plans to expand support for more tools and data types.

This is a VS Code-based MCP server template project for external reconnaissance tasks. The project uses the uv tool for environment management and dependency installation, supports Python 3.11+ versions, and includes the dnspython dependency and a custom external-recon.py script.

An MCP server that provides a Python code execution environment, supporting code running, package installation, and file operations, suitable for integration with LLM applications.

EvolveMCP is an open - source project that enables Claude AI to independently build, install, and use tools, expanding the practical capabilities of AI through Python code execution.

The Modal MCP Toolbox is a collection of tools running on the Modal platform. Based on the Model Context Protocol (MCP), it can provide extended functions for LLMs, such as Python sandbox code execution and FLUX model image generation.