Overview of the MCP LLM Sample Server

The MCP LLM Sample server demonstrates how to expose lightweight, domain‑specific functionality to AI assistants via the Model Context Protocol. By running three distinct MCP servers—each listening on its own port—the sample showcases a modular approach to building AI‑enabled services that can be discovered, invoked, and combined by client applications such as Claude or other MCP‑aware assistants.

What Problem Does It Solve?

Developers often need to augment AI assistants with external knowledge or specialized computation without rewriting the core model. The sample servers provide ready‑made endpoints for common tasks—basic arithmetic, weather querying, and LLM selection—that illustrate how to encapsulate these operations as MCP resources. This removes the need for custom integrations, allowing assistants to request precise actions from external services while maintaining a clean separation between model logic and domain logic.

Core Functionality and Value

Each server implements one or two simple functions:

• Math Server – performs basic arithmetic operations (add, subtract, etc.).
• Weather Server – retrieves current weather data for a specified location.
• LLM Selector Server – decides which language model to use based on input constraints.

These services expose resources, tools, and prompts that an MCP client can consume. By exposing such capabilities, developers give AI assistants the ability to invoke real‑world actions (e.g., fetch weather) or delegate calculations to a reliable backend, thereby extending the assistant’s usefulness without compromising security or reliability.

Key Features Explained

• Modular Architecture – Each server runs independently on a dedicated port, enabling easy scaling and isolation.
• Resource‑Based API – Functions are exposed as named resources that can be queried or called by name, making discovery straightforward.
• Prompt Integration – The servers provide sample prompts that guide the assistant on how to format requests, ensuring consistency across clients.
• Sampling Control – Clients can specify sampling parameters (temperature, top‑p) to fine‑tune the assistant’s responses when interacting with these services.

Real‑World Use Cases

1. Conversational Agents – A chatbot can answer user queries about the weather or perform quick calculations on demand, enhancing interactivity.
2. Dynamic Model Selection – The LLM selector allows an application to route requests to the most appropriate model (e.g., a smaller, cheaper model for quick responses or a larger one for complex reasoning).
3. Hybrid Workflows – Developers can combine multiple MCP servers to build composite services (e.g., a travel planner that fetches weather, calculates distances, and selects the best itinerary).

Integration with AI Workflows

The MCP client acts as a bridge between the user interface (a Streamlit web app in this sample) and the MCP servers. When a user submits a query, the client determines which resource to invoke, formats the request according to the server’s prompt schema, and streams the response back to the assistant. This pattern allows developers to plug new services into existing AI pipelines with minimal friction, preserving the modularity that MCP encourages.

Standout Advantages

• Zero Boilerplate – The sample servers require only a few lines of Python, making it trivial to prototype new capabilities.
• Portability – Because MCP is language‑agnostic, the same server can be consumed by any client that understands the protocol.
• Transparent Discovery – The servers expose clear resource names and schemas, enabling automated tooling to generate documentation or UI components on the fly.

In summary, the MCP LLM Sample server set provides a practical blueprint for extending AI assistants with external functionality. By encapsulating domain logic behind well‑defined MCP resources, developers can rapidly build, test, and deploy services that enhance conversational agents without compromising on security or maintainability.