Python MCP SSE Server

Py Mcp Sse – A Cloud‑Native MCP Server for Real‑Time Tool Access

This project delivers a lightweight, SSE‑based Model Context Protocol (MCP) server that exposes the Gutenberg API as an AI tool. By moving from the traditional STDIO approach to a streaming server model, it enables developers to host MCP services as independent processes that can be accessed from anywhere in the network. The server runs a single, well‑defined tool () that accepts search terms and returns a list of relevant literary works. The companion client demonstrates how an AI assistant can discover, invoke, and consume the results of this tool in a conversational flow.

The core problem solved by this MCP server is the tight coupling that exists in STDIO‑based examples, where the client and server must run in a single process. In many production or cloud‑native scenarios, assistants need to reach out to remote services over HTTP/HTTPS. By leveraging Server‑Sent Events (SSE), the server can push updates and tool responses to any number of connected clients without requiring a persistent WebSocket or heavy orchestration. This stateless, event‑driven design makes scaling, deployment, and fault isolation straightforward.

Key capabilities of the server include:

• Tool discovery: Clients can query available tools and their signatures at runtime, allowing dynamic integration of new functionalities.
• Streaming responses: Results are sent incrementally via SSE, which is ideal for large datasets or long‑running queries such as searching a vast digital library.
• OpenRouter integration: The server uses OpenRouter.ai to route LLM calls, enabling flexible choice of underlying language models without modifying the MCP contract.
• Environment‑driven configuration: Sensitive credentials (e.g., OpenRouter API key) are supplied through environment variables, keeping secrets out of source control.

Typical use cases span from interactive research assistants that pull book recommendations to educational platforms that fetch curriculum resources on demand. A developer can embed this server in a microservice architecture, letting any AI assistant—whether hosted on the cloud or locally—reach out to search literature without handling API keys or pagination logic. Because the server is decoupled, it can be upgraded independently of client code, and multiple assistants can share a single instance, reducing operational overhead.

In practice, the integration workflow is simple: an assistant initiates a conversation, discovers , invokes it with user‑supplied terms, and receives a streamed list of titles. The client’s conversational loop then presents these results back to the user, optionally prompting for further refinement. This pattern exemplifies how MCP servers can offload domain‑specific logic from the LLM, keeping prompts concise while still delivering rich, contextually relevant data.