"Python runner with streamable HTTP support" matching MCP servers:

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 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.

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

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.

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.

Execute Python code via subprocess with timeout and error handling, returning stdout, stderr, and exit code.

Executes Python code in isolated subprocesses with resource limits and timeout, designed for safe execution in Docker containers.

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.

Enables AI assistants to execute Python functions and system commands via Streamable HTTP, including bash, Python code execution, file operations, and text searching.

Enables execution of Python code in a safe environment, including running scripts, installing packages, and retrieving variable values. Supports file operations and package management through pip.

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.

Exposes MAID Runner validation tools (validate, snapshot, test) as MCP tools, resources, and prompts for AI agents like Claude Code and Aider, enabling seamless integration with AI development workflows.

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 an ElevenLabs support agent to call support tools over MCP, including Discord ticket flows, post-call webhook handling, and knowledge-base syncing.

Enables AI clients to list, inspect, triage, and update support tickets via MCP tools, with a mocked dataset designed for swapping in real ticket sources later.

Enables controlled development and staging operations through audited tools for project status, testing, services, databases, deployments, logs, backups, and rollbacks.

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 production-grade MCP server providing a persistent Python REPL with multi-session support, sandboxing, and timeout protection, enabling LLM agents to execute Python code across multiple turns with variables that persist between calls.