"Simple MCP example using HTTP transport" matching MCP servers:

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.

stateful MCP server with Redis sessions, event sourcing, and horizontal scaling

Demonstrates building an MCP server with Python, exposing tools like web search (Tavily) and basic utilities over streamable HTTP for AI assistants like Cursor.

stateless MCP server — fresh instance per request, infinite horizontal scaling

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.

Provides Estonian public transport timetables, trip planning, stop search, and real-time vehicle tracking.

Enables MCP clients to connect over HTTP/SSE, call simple echo, arithmetic, uppercasing, and time tools, and run asynchronous tasks with approval elicitations via the Tasks extension.

Enables fetching HTTP resources through the MCP protocol.

A simple MCP server implementation in Python using FastMCP and FastAPI, providing streamable HTTP transport with optional OAuth authorization for tools like echo and math.

Mock HTTP server implementing MCP (Model Context Protocol) with streamable HTTP transport, health endpoint, and example tools like echo and time.

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.

Enables AI agents to access real-time public transport data for Moscow and the Moscow region, aggregating and reconciling information from multiple sources. Provides tools for finding stops, live arrival boards, vehicle tracking, route details, and ETA calculations.

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 journey planning, live departures, service alerts, and commuter car-park occupancy for Sydney and New South Wales across trains, metro, buses, ferries, and light rail, accessible via natural language queries.

A demonstration MCP server that fetches web content and monitors system information via SSE.

Enables searching for Auckland public transport stops and retrieving real-time transit schedules and timetables using the Auckland Transport API with GTFS standardized data.

Enables browsing and fetching transport and mobility news feeds from curated sources and arbitrary RSS/Atom/RDF URLs.

Enables querying Swiss public transport (trains, buses, trams, boats) for stations, connections, and station boards via the opendata.ch API.

A basic example of a Model Context Protocol (MCP) server deployed as a Netlify Function, demonstrating how to serve MCP endpoints over HTTP.