Microsoft Copilot Studio ❤️ MCP
# Microsoft Copilot Studio ❤️ MCP This lab has been moved to [here](https://aka.ms/mcsmcplab).
Find MCP servers that connect AI assistants to useful tools and data. Explore what each server does, its setup and its documentation.
2703 servers · Page 160 of 226
# Microsoft Copilot Studio ❤️ MCP This lab has been moved to [here](https://aka.ms/mcsmcplab).
Diagrams, Photo labeling and explanations directly in Claude Chat
SAT score percentiles and class of 2026 SAT averages and participation by state (College Board).
# DeepSeek MCP Server Model Context Protocol server for the current DeepSeek V4 API. As of April 24, 2026, DeepSeek's public API reference documents…
A daily feed of AI releases, tools and techniques, each with a verification status, source trust score and evidence links. Read-only MCP server, no...
Search and work in Gmail, Calendar, Drive, Docs, Sheets and Slides across several Google accounts.
Make 2D videos and 3D films from a written brief, in your AIVideo.day account.
Network Sketcher (Local MCP) is an AI-native network design and diagramming tool that exposes the Network Sketcher engine as a Model Context Protocol…
Connect, call and coordinate AI agents, hosted or self-hosted. HTTP, MCP, A2A and Go/TS/Python SDKs; Apache-2.0 Core with traceable runs.
# Neo4j MCP Clients & Servers Model Context Protocol (MCP) is a [standardized protocol](https://modelcontextprotocol.io/introduction) for managing context between large language models (LLMs) and external…
Transcribe recorded audio and video from direct HTTPS media URLs, retrieve speaker-labelled transcripts with timestamps, export TXT/SRT, and check account...
Run advanced phishing simulations and cybersecurity awareness training from your AI assistant.
An MCP server exposes tools or data through the Model Context Protocol so a compatible AI client can use them. Each project has its own installation, authentication and permission requirements.
Open a server’s page for its getting-started notes, compatibility and sources, then follow the original project’s documentation for your AI client.
Entries link to the original documentation. Check access requirements, costs and maintenance there before installing, and grant a server only the access it needs.