Kaltura-MCP server powering agentic AI workflows with Kaltura's API

A collection of MCP servers.

A lightweight, self-hosted MCP server for persistent team/project memory

Claude Code plugin marketplace for the n24q02m MCP servers -- install web search, memory, Notion, email, Telegram, Godot, code review, and image/video tools in one command.

MCP365 Explorer — Open-source SPFx webparts for exploring Microsoft 365 MCP servers (Agents 365 Tools)

The MCP Integrity Standard defines a specification for verifying the integrity, provenance, and behavioral declarations of tools and servers operating over the Model Context Protocol (MCP)

Jira Tempo time tracking MCP server for Cursor and Claude Desktop

MCP server that gives AI models access to kRPC Python documentation

MCP server to allow LLMs to generate speech on local systems

AI in your browser that supports MCP Servers, no network call, no costs, 100% private

MCP server for headless Excalidraw diagram rendering. Renders locally — no data sent to third-party servers.

Security scanner for AI packages — MCP servers, skills, npm/pip. CLI tool.

MCP server for ffl. Let AI share anything for you.

MCP server for seamless context transfer between AI conversation threads. Built with FastMCP.

👉 🤖 Hands-on demos to master the GitHub Copilot SDK: basics, agents, MCP servers, and real-time streaming

Search more, hallucinate less - a suite of MCP servers and CLI tools to supercharge your LLM skillset

Voltaic is an open source JSON-RPC 2.0 and MCP client and server.

Console tool to deploy and monetize MCP servers in the cloud

Broxy fronts multiple upstream MCP servers as one, aggregates capabilities across protocols, and exposes only what presets allow.

MCP server for Dex personal CRM with fuzzy contact matching and enrichment tools

Perplexity MCP server with some pro options, multiple deployment and full API support

MCP server that provides tools and resources for interacting with n8n API

Tools and an MCP server for building Kubernetes agents

IntelliCore is an AI-driven KMS leveraging RAG (Retrieval-Augmented Generation), MCP (Model Context Protocol), vector embeddings, and LLMs for intelligent document querying.