"Open-source API development tools for Linux with Markdown support" matching MCP servers:

Enables AI assistants to perform controlled Linux system administration tasks like reading logs, managing services, cron jobs, WordPress, and executing sandboxed Python code, with strict security constraints.

Exposes a Linux host with shell, Python, and filesystem tools as MCP tools over HTTPS, enabling AI agents to execute commands and manage files remotely.

Facilitates spec-driven development workflows by providing structured prompts for generating requirements in EARS format, design documents, and implementation code following a systematic approach.

Enables chat clients to execute real shell commands (apt install, curl, ffmpeg, Python) inside a disposable, per-conversation Debian container and hand resulting files back to the user via signed download links. Installed packages and processes are discarded when the container recycles, while files under /workspace persist.

Enables running Claude Skills locally on your Mac with sandboxed execution, supporting document processing and integration with MCP-compatible AI tools while ensuring data privacy.

An MCP Server that enables natural language interaction with the Open Policy Agent REST API, allowing users to manage policies, decisions, and data through conversational interfaces.

An MCP server that provides a persistent sandbox for AI coding agents to explore codebases server-side, returning only compact summaries to reduce context consumption.

A meta-MCP server that orchestrates tools from multiple MCP servers, enabling complex Python workflows with loops and conditionals.

Enables AI agents to invoke a local Stata installation, including a Windows Stata build under Wine on Linux and macOS, for regression analysis, econometrics, paper replication, and empirical research with command guards, resource monitoring, and automatic log capture.

Enables Large Language Models to access and manipulate Autodesk Revit models through a pyRevit-based bridge and the Model Context Protocol.

A minimal MCP server that exposes a run_command tool over stdio, enabling command execution within a Docker container.

Enables Claude Agent SDK agents to safely operate a remote user machine via a WebSocket tunnel, providing six client__* MCP tools with path fencing and phase-switch adjudication.

Enables coding agents to perform file, search, patch, git, process, test, package, network, and system operations through 60 typed MCP tools with structured inputs/outputs, structured errors, and a full event journal, replacing terminal use with a typed machine API.

Provides a Docker-based development environment with Python and Node.js MCP servers, enabling file operations, SQL queries, Redis caching, and debugging for building and testing MCP servers.

Empower any MCP-compatible AI Agent(MCP Client) with engineering-grade capabilities to understand, modify, run, and deliver real-world code repositories.

Enable Claude (or any other LLM) to interactively debug your code (set breakpoints and evaluate expressions in stack frame). It's language-agnostic, assuming debugger console support and valid launch.json for debugging in VSCode.

MCP server that gives any LLM a managed Docker workspace with live browser, terminal, code execution, document skills, and autonomous sub-agents.

Enables AI agents to control a live Blender session for scene/object manipulation, rendering, code execution, and PolyHaven asset/texture management. It also supports runtime switching among Ollama, LM Studio, llama.cpp, OpenAI-compatible, and Azure backends.

Enables natural language control of Blender 3D through local AI models, allowing MCP clients to create, modify, and render scenes, apply materials, and execute Python code in Blender.

Lets AI agents drive a live Blender session — reading scene and object data, creating, modifying and deleting objects, setting materials, rendering images, executing code, and downloading PolyHaven assets. Prompts flow through a swappable, provider-agnostic LLM backend (Ollama, LM Studio, llama.cpp, OpenAI-compatible, Azure) that can be reconfigured at runtime without restarting.

MCP server for the Agent Platform API, enabling management of sandbox environments through MCP protocol. Exposes tools to interact with sandbox lifecycle operations such as start, stop, and status checks.

Enables AI agents to execute tools securely with Docker isolation, tier-based permissions, and feature flags, providing a centralized tool registry and MCP server.

A Model Context Protocol (MCP) server that provides TypeScript development tools for automated refactoring and code analysis.

Extends VS Code Copilot with MCP tools for persistent semantic memory, code indexing and RAG search, sandboxed Python/shell execution, web fetch/search, dynamic tool management via a web UI, and structured multi-step reasoning.

Enables AI agents to control a live Blender session — inspecting and editing scenes, objects and materials, rendering images, executing code, and pulling PolyHaven assets — through MCP tools. It also routes prompts to a provider-agnostic LLM backend (Ollama, LM Studio, llama.cpp, OpenAI-compatible, or Azure) that can be switched at runtime.

Enables AI agents to control a live Blender session via MCP tools for scene and object operations, rendering, code execution, and PolyHaven asset management. It supports provider-agnostic LLM backends that can be configured at startup or switched at runtime.

Enables AI agents to control a live Blender session with scene/object manipulation, rendering, PolyHaven asset tools, and runtime-switchable LLM provider backends.

A collection of production-ready MCP tools for common tasks such as web search, code execution, file operations, and database queries, enabling AI agents to perform these actions through a unified interface.

Enables AI agents to drive a live Blender session over MCP — inspecting scenes, creating, modifying and deleting objects, applying materials, rendering images, importing PolyHaven assets, and executing code — while routing prompts through a provider-agnostic LLM backend (Ollama, LM Studio, llama.cpp, OpenAI-compatible, Azure) that can be switched at runtime.

MCP server that gives AI agents isolated, named Linux computers with stable IDs and lifecycle control. Supports multiple backends like Docker, Fly Machines, Modal, and E2B, with a dashboard for managing sandboxes.