"Techniques for Writing Strong Code in C++, Rust, and Python" matching MCP servers:

Blazing-fast, asynchronous MCP server for seamless filesystem operations.

Enables LLMs to interact with Python environments, execute code, manage files, and handle packages through the Model Context Protocol.

A Model Context Protocol server that allows LLMs to interact with Python environments, execute code, and manage files within a specified working directory.

Enables LLMs to run Python code and scripts across system and conda environments, list and install packages, and read or write text and binary files within an isolated working directory. It also provides prompt templates for generating, refactoring, and debugging Python code.

Enables LLMs to manage Python environments, execute Python code and scripts, install packages, and read or write files securely within a configured working directory.

Enables LLMs to manage Python environments, execute code and scripts, install packages, and read or write files within an isolated working directory.

Enables LLMs to interact with Python environments, run code and scripts, manage packages, and read or write files within a configured working directory.

Enables LLMs to execute Python code and scripts, manage Python environments and packages, and read or write files within an isolated working directory. It also provides prompt templates for common Python tasks such as function creation, refactoring, and debugging.

Enables LLMs to interact with local Python environments, execute code and scripts, manage packages, and read or write files within an isolated working directory.

Enables LLMs to run Python code and scripts, manage Python environments and packages, and read or write files within an isolated working directory. It also provides prompts for common Python tasks such as function creation, refactoring, and debugging.

Enables LLMs to interact with Python environments, execute code and scripts, manage packages, and read or write files within an isolated working directory.

Enables LLMs to interact with Python environments, execute code or scripts, manage packages, and read/write files within an isolated working directory.

Enables LLMs to execute Python code and scripts across system and conda environments, manage installed packages, and read or write text and binary files within an isolated working directory.

Enables LLMs to work with local Python environments by listing environments and installed packages, installing dependencies, and running code or scripts. Also supports reading, writing, and listing files within a sandboxed working directory.

Enables LLMs to manage Python environments and packages, execute Python code or scripts, and read or write files within a secured working directory. It also provides prompts for common Python tasks such as function creation, refactoring, and debugging.

Enables LLMs to interact with Python environments, execute code, manage packages, and perform file operations within a configurable working directory.

Enables LLMs to run Python code and scripts across available environments, manage packages, and read/write files within a secured working directory. It also provides prompts and resources for common Python development tasks.

Enables LLMs to execute Python code and scripts across system and conda environments, manage installed packages, and read or write text and binary files within an isolated working directory. It also exposes prompts for generating, refactoring, and debugging Python code.

Enables LLMs to interact with Python environments, execute code or scripts, manage packages, and read/write files within an isolated working directory.

Enables LLMs to manage Python environments, execute Python code and scripts, install packages, and read or write files within an isolated working directory.

Enables LLMs to inspect and use Python environments, execute code or scripts, manage installed packages, and read or write files within an isolated working directory.

Enables LLMs to execute Python code and scripts across system and conda environments, manage installed packages, and read or write text and binary files within an isolated working directory. It also exposes environment, package, and file resources plus prompt templates for creating, refactoring, and debugging Python code.

Enables LLMs to manage Python environments, execute code and scripts, install packages, and read/write files within an isolated working directory. It also provides prompts for common tasks such as function creation, refactoring, and debugging.

Enables LLMs to manage Python environments, execute code and scripts, install packages, and read or write files within an isolated working directory through MCP.

Enables LLMs to manage Python environments, execute code and scripts, install packages, and read or write files within an isolated working directory.

Enables LLMs to execute Python code, manage environments and packages, and read or write files within an isolated working directory through the Model Context Protocol.

Lets LLMs list and use Python environments, execute code and scripts, manage packages, and securely read/write files within a configured directory.

Zero-trust PII & secret airlock for coding agents (reversible <TOKEN_N> file read/write sanitization, safe subprocess runner with stdout scrubbing, and 0.2ms Git pre-commit scanning via pure-ONNX GLiNER + regex).

50 tools and 400 functions for working with Excel/.xlsx spreadsheets — read/write, recalculate formulas, diff, repair broken references, and audit. Built for AI agents.

A Python MCP server that generates complete Vue 3 CRUD structures with Composition API, including components, services, stores, and routes.

MCP server for copying and pasting code between files with security controls and validation.

A secure, read-only MCP server for browsing and searching files in a specified directory with path traversal protection and .gitignore support.

MCP server wrapping Kimi Code CLI (kimi-k2.5) to provide tools for filesystem, shell, web, and agent operations.

Enables Claude to list directories, read text files, and search code within a designated read-only project folder, while skipping common dependency folders and blocking sensitive files.

An experimental MCP server that enables AI assistants to interact with VS Code workspaces through file operations, code execution, and Git management. It also provides tools for Docker integration, project scaffolding, and secure command execution using project-specific configurations.

A comprehensive MCP server that provides AI assistants with tools for file system management, Git integration, and shell command execution. It features specialized code utilities for analysis, formatting, and linting to enhance development workflows within Claude Desktop.

A full-featured secure MCP server for local file system operations, with built-in image processing, OCR and media tools. Fully compliant with the official Model Context Protocol specification, offering standardized request/response schemas, large-file streaming I/O, multi-transport remote deployment, and comprehensive text search & replace functionality for LLM agent integration.

Enterprise-grade MCP server integrating Claude AI capabilities, enabling AI-powered conversations, file operations, and system tools via the Model Context Protocol.

Enables file reading with pagination and regex-based code search across a codebase using grep.

Enables Claude AI to run terminal commands, perform file operations, and manage git in a project directory via the Model Context Protocol.