"PostHog: Open-source product analytics platform" matching MCP servers:

The Fabric-Analytics-MCP server enables AI agents to interact directly with Microsoft Fabric using natural language. It transforms complex data engineering tasks—such as workspace management, data exploration, and job execution—into intuitive, conversational workflows for LLMs like Claude or GitHub

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.

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.

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.

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 Model Context Protocol server that enables AI models to access tools through GitHub authentication and provides comprehensive analytics tracking of tool usage, performance, and user behavior.

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.

A modular platform that enables LLM agents to discover, register, and execute both local tools and tools from external MCP servers, with REST APIs for server management and Streamable HTTP support.

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 AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.

AI-driven tool that helps developers create beautiful UI components instantly through natural language descriptions, integrating with popular IDEs like Cursor, Windsurf, and VSCode.

Turns any LLM into a coding co-pilot with production-ready MCP server providing lint, test, execution, generation, and graph tools. Features Neo4j persistence, OpenAI Agent integration, REST API, and React frontend for comprehensive code development assistance.

A platform that transforms AI development with intelligent context management, optimization, and prompt engineering, enabling developers to enhance model performance through structured context management and optimization tools.

Enables AI agents to interact with the Roark API by writing and executing TypeScript SDK code in an isolated sandbox, with built-in documentation search to guide those tasks.

Enables fully local, end-to-end research automation: reproducing papers from PDF into runnable code and verified artifacts, writing papers section-by-section with LaTeX/PDF/DOCX export, and running an ideate→debate→design→verdict research pipeline—all without an API key.