"MCP servers with streamable-http support" matching MCP servers:

Demonstrates how to create a simple MCP server with streamable HTTP transport, featuring tools, prompts, and resources.

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 Python-based MCP server that exposes tools over streamable HTTP using FastAPI, enabling connection to AI assistants like Cursor. Supports multiple servers mounted in a single FastAPI app.

Provides basic arithmetic calculation tools through an HTTP-accessible MCP server. Supports mathematical operations like addition with streamable responses for integration with MCP clients.

Enables exposing MCP tools over HTTP using Python and FastAPI, allowing AI assistants like Cursor to connect and use tools like web search, echo, and math operations.

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

Transforms stdio-based task tools into a stateful Streamable HTTP MCP server, enabling multiple clients to manage tasks via URL with session isolation and security checks.

A starter template for building MCP servers in Python using the streamable HTTP transport protocol. Provides a foundation with the MCP Python SDK and example configuration to quickly develop custom MCP servers.

A Docker-based deployment that wraps the Gemini MCP server with supergateway to expose its tool capabilities over Streamable HTTP. It enables users to perform image-editing workflows and interact with the Google Gemini API using a standardized web-accessible endpoint.

A tool that enables automated browser control using Pyppeteer within the MCP framework, allowing navigation, screenshot capture, and element interaction with websites.

Enables hybrid search (BM25 + semantic with RRF) over documentation, with Thai language support. It exposes MCP tools for adding documents and searching via Qdrant and Ollama/OpenAI, using a stateless Streamable HTTP transport.

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.

Enables AI assistants to work with Chapel code, including compiling, linting, and accessing educational primers.

Provides a framework for running multiple MCP servers using FastAPI, stdio, and streamable-http transports, with an echo tool for testing and integration with MCP Inspector.

An MCP server that provides HTTP client capabilities with support for full HTTP methods, custom headers, query parameters, request bodies, and configurable timeouts.

Enables AI harnesses to maintain a persistent memory layer backed by a local SQLite file, providing MCP tools to add, search, deprecate, and synchronize facts without deleting history.

A learning MCP server that demonstrates tools and resources using FastMCP, with a greet tool and UI capabilities.

Manages the state of a chess game for LLMs/agents without suggesting moves, providing tools to create/reset game, add moves, get status, and check legality.

Provides seamless access to the Protein Data Bank in Europe (PDBe) API and search capabilities, enabling AI clients to query protein structures, perform advanced searches, and retrieve structural biology data.

Enables interaction with Jenkins servers through HTTP APIs, allowing users to list jobs, trigger builds, view logs, and manage configurations without requiring plugins or admin access.