Simple Model Context Protocol MCP

The Simple Model Context Protocol (MCP) Server is a lightweight, demonstrative implementation that showcases how the MCP can be used to bridge large language models (LLMs) with external data stores and tools. By exposing a set of well‑defined endpoints, the server allows an AI assistant to perform CRUD operations on notes stored in a SQLite database and to invoke simple utility tools such as random‑seed generation and echoing. This makes it a practical example for developers who want to see how MCP can be leveraged to enrich LLM interactions with persistent, structured data and custom functionality.

What problem does it solve?

Modern AI assistants often need to access real‑time data or perform actions that go beyond pure text generation. Without a standard way for the LLM to call external services, developers must write bespoke adapters or rely on ad‑hoc integrations. The MCP Server solves this by providing a conventional, protocol‑driven interface that any LLM capable of speaking MCP can use. It removes the friction of custom API design, ensuring that context and data flow smoothly between the model and the underlying system.

How does it work?

The server exposes a set of MCP endpoints:

• CRUD for notes – Clients can create, read, update, or delete textual notes. These operations are backed by an SQLite database accessed via Entity Framework Core, giving developers a simple persistence layer.
• Utility tools – Two lightweight tools are available: one that generates a random seed and another that echoes the user’s input. These illustrate how arbitrary logic can be packaged as callable tools within MCP.
• Model context exchange – The server follows the MCP specification for packaging and exchanging context, enabling an LLM to receive structured data (e.g., a list of notes) and to send back commands or requests.

Because the server adheres strictly to MCP, any AI assistant that implements the protocol can seamlessly interact with it, regardless of language or framework.

Key features and capabilities

• Standardized communication – Uses the MCP wire format for all requests, guaranteeing interoperability with any compliant LLM.
• Persistent storage – Notes are stored in a lightweight SQLite database, making the example realistic yet easy to set up.
• Tool integration – Demonstrates how custom tools (random seed, echo) can be exposed and invoked by the model.
• Entity Framework Core – Provides a familiar ORM for developers, abstracting raw SQL and simplifying data access.
• Cross‑platform – Built on .NET Core 9.0, it runs on Windows, macOS, and Linux without modification.

Real‑world use cases

• AI‑powered note taking – An assistant can create, retrieve, and edit notes on demand, keeping a persistent record that the model can reference during conversations.
• Contextual data retrieval – Developers can replace the note store with any database or API, allowing the model to fetch real‑time data (e.g., weather, stock prices) through a uniform interface.
• Custom tool invocation – The echo and seed tools serve as templates for more sophisticated operations, such as calling external services or performing domain‑specific calculations.
• Rapid prototyping – Because the server is minimal and follows MCP, it can be used as a starting point for building production‑grade AI applications that need reliable data access.

Integration with AI workflows

In practice, a developer would run the MCP Server and point their LLM client (e.g., Claude or another model) at its endpoint. The model can then:

1. Request a list of notes – The server returns the current context, which the model can incorporate into its response.
2. Create or update a note – The model sends an MCP command, and the server persists the change.
3. Invoke a tool – The model calls the echo or seed tool to perform quick calculations or generate deterministic values.

Because all interactions follow the same protocol, developers can swap out the underlying database or add new tools without touching the LLM code. This modularity makes MCP an attractive choice for building scalable, maintainable AI systems that need to stay in sync with external data sources.