Starlette MCP SSE Server

Overview

The Starlette MCP SSE server is a lightweight yet fully‑featured web application that demonstrates how to expose Model Context Protocol (MCP) capabilities over Server‑Sent Events (SSE) using the Starlette framework. By combining MCP’s real‑time streaming with a standard HTTP API, it solves the common problem of making large language models (LLMs) continuously aware of external data sources without re‑training or heavy latency. Developers can now have AI assistants that receive live updates, execute tool calls, and stream responses back to clients in a single, coherent endpoint.

This server offers a dual‑purpose architecture: the and endpoints implement MCP’s SSE contract, while conventional web routes (, , , ) provide a normal RESTful interface. The separation of concerns keeps the AI‑centric logic isolated from generic web functionality, allowing teams to evolve each layer independently. The implementation is intentionally modular; new tools can be added by extending the MCP handler, and custom routes can be inserted without touching the core SSE logic.

Key capabilities include:

• Live streaming of tool responses: The SSE endpoint pushes incremental messages as the AI processes requests, enabling real‑time feedback in client UIs.
• Tool discovery and invocation: Clients can list available functions (e.g., , ) and invoke them with parameters, all defined through the MCP schema.
• Status monitoring: A lightweight API offers health checks, making the server suitable for containerized deployments and orchestration platforms.
• Interactive documentation: The route automatically generates OpenAPI docs for the web endpoints, while MCP Inspector can be configured to test and debug the SSE interface.

Typical use cases span from weather‑aware chatbots that stream live alerts to financial assistants that pull market data on demand. In a microservices architecture, the MCP SSE server can act as a bridge between an LLM and domain‑specific APIs, ensuring that the model’s responses are always grounded in current data. Its Starlette foundation guarantees high performance, async handling, and easy integration with existing Python web stacks.

What sets this server apart is its single‑endpoint, real‑time approach to MCP. By leveraging SSE, the model can send partial results and receive tool calls without the overhead of polling or WebSocket management. This design reduces network chatter, simplifies client implementations, and aligns closely with how modern web browsers handle streaming responses. For developers building AI‑powered applications that need both robust web services and dynamic, tool‑enabled LLM interactions, the Starlette MCP SSE server provides a ready‑to‑deploy, standards‑compliant foundation.