A multi-domain MCP server that exposes a small, stable surface of three tools (list_domains, list_domain_tools, call_domain_tool) to gate access to various tool domains like Plane and BloodBank, preventing agent schema overload.
A deep-research MCP server exposing a single task-augmented tool that runs GPT-Researcher with Tavily search, Anthropic Claude, and OpenAI embeddings, streaming progress via MCP tasks and Upjack entity stream.
A local MCP server that lets any LLM agent manage Pine AI tasks — negotiate bills, cancel subscriptions, resolve disputes, and make phone calls on your behalf.
A Python MCP server that enables AI assistants to manage Todoist tasks and projects through the Model Context Protocol. It supports full CRUD operations for tasks and projects, including support for nested projects and Todoist's advanced filter syntax.
A FastMCP-based server providing a comprehensive toolset for the RunningHub AI application platform. It enables users to configure nodes, upload media, submit AI tasks, and manage task execution and results through persistent storage.
MCP server for managing a media server stack (Plex, Radarr, Overseerr, Bazarr, Prowlarr, Trakt.tv) using natural language to browse, request, and discover content.
A minimal MCP server template with a calculator module, designed for learning NitroStack fundamentals. It includes tools, resources, prompts, and TypeScript validation.
MCP zero-trust gateway that sits in front of every internal MCP server, detects tool-poisoning/metadata drift in real time, and maintains a cryptographic provenance ledger of every agent tool call.
A minimal MCP server template that offers calculator tools, resources, and prompts for learning NitroStack fundamentals, built with TypeScript and Zod.
An MCP server framework featuring dynamic tool loading and a modular one-tool-per-file architecture for rapid development. It supports both Stdio and HTTP transport modes, offering automated test generation and centralized configuration management.
A Model Context Protocol server framework featuring dynamic tool loading and automatic tool discovery from a dedicated directory. It leverages FastMCP to provide a robust environment for building, configuring, and testing individual Python-based tools.
A Model Context Protocol server built with FastMCP that enables dynamic tool loading and configuration from individual Python files. It provides a flexible framework for automatically discovering, testing, and running tools via Stdio or HTTP transport modes.