Provides tools for AI agents to query live public transport data for GZM (Silesian Metropolis) including vehicle positions, trip updates, service alerts, and next arrivals.
Demonstrates building an MCP server with Python, exposing tools like web search (Tavily) and basic utilities over streamable HTTP for AI assistants like Cursor.
A reference implementation demonstrating proper MCP server patterns with HTTP transport, featuring session management, progress notifications, and example tools for testing server functionality. Serves as a clean template for building MCP servers with streamable responses and comprehensive error handling.
Provides a generic HTTP client tool allowing AI agents to make arbitrary HTTP requests from their environment, with support for environment-variable placeholders for secrets.
HTTP-4-MCP configuration tool allows you to easily convert HTTP API into MCP tool, writing the code written. Through simple interface operation, you can quickly configure an mcp-server.
A high-performance WebSocket/SSE transport layer and gateway for Anthropic's MCP, enabling real-time, scalable AI integrations with LLM agents like Claude.
Enables searching for Auckland public transport stops and retrieving real-time transit schedules and timetables using the Auckland Transport API with GTFS standardized data.
A step-by-step guide and complete working example for building and running an MCP server with streamable HTTP transport using Python, mcp, and FastAPI, enabling AI assistants to access tools over HTTP.
A Multi-Agent Conversation Protocol server that enables interaction with Rajasthan Transport Department's API, allowing access to vehicle and driver-related services through natural language.