"Configuration Settings for Directory Mounting and Environment Management" matching MCP servers:

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

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.

Gives LLM agents spatial awareness inside Autodesk Maya, enabling scene inspection and measurement, aesthetic and engineering audits, camera planning, checkpoint/rollback safety, asset import, and viewport or render previews. Agents can also execute arbitrary Python in Maya to build and verify 3D scenes against engineering rules instead of writing blind.

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.

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.

A testing environment for MCP server setup and client-server interactions, allowing users to verify basic MCP server functionality and test client-server communications including shell command execution.

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.

A Python server implementing the Model Context Protocol to provide customizable prompt templates, resources, and tools that enhance LLM interactions in the continue.dev environment.

A starter template for creating MCP servers that work with Puch AI, featuring ready-to-use tools for job searching and image processing. Includes examples for Bearer token authentication, OAuth integration with Google and GitHub, and demonstrates user-scoped data management.

Enables AI assistants to operate directly inside a running Unity Editor project, using built-in tools for scene creation, script generation, play mode automation, input simulation, and performance analysis.

A simple server that acts as a Master Control Program (MCP) for unified interaction with OpenAI and Anthropic (Claude) AI models through a single API endpoint.

Enables AI clients to interact with and control the Unity Editor through a Python MCP server bridge, allowing natural language-based Unity project manipulation.

Enables reproducible evaluation of AI coding agents by exposing repository inspection, code editing, test running, and deterministic verification through MCP tools.

A Model Context Protocol server implementation that provides a simple interface to interact with PostgreSQL databases, enabling SQL queries, database operations, and schema management through MCP.

A serverless implementation of the Model Context Protocol (MCP) that runs on Netlify Functions, allowing developers to build and deploy MCP-compatible services with minimal configuration.

Enables Windows users to connect Antigravity CLI as an MCP subagent for coding tasks such as file reading, code execution, edits, and terminal commands, with model selection, conversation continuity, and streaming progress.

Connects Claude Desktop to a running FreeCAD instance, enabling document management, part creation and editing, direct Python execution, parts library insertion, FEM analysis, and 3D view screenshots.

An MCP server that lets Large Language Models interact with Stata software to perform regression analysis and other statistical operations.

Enables Claude Desktop to control FreeCAD for 3D CAD modeling, including creating, editing, and deleting objects, executing Python code, and running FEM analyses.

An MCP server that grants AI agents unrestricted file system, Python, and PowerShell access on Windows for real, unfiltered automation.