南哥AGI研习社 MCP Series

Mcpservertest – A C#‑Based Model Context Protocol Server

The Mcpservertest repository demonstrates how a lightweight MCP server can be built with the Microsoft C# SDK, enabling developers to expose custom logic as AI‑assistant tools. The primary problem it solves is the lack of a straightforward, language‑agnostic way to turn ordinary .NET code into callable services for AI assistants like Claude. By wrapping simple string manipulation functions (echo, reverse, length) into MCP tools, the server shows how to bridge local codebases with remote LLMs without writing extensive boilerplate.

At its core, the server registers methods as MCP tools using the attribute. This declarative approach lets the LLM interpret the tool’s purpose from its description and automatically decide when to invoke it. Once the server is running, developers can add a configuration in VS Code to register the service with the IDE’s MCP client. The result is a seamless workflow where an AI assistant can call these tools directly from the chat interface, returning instant, deterministic results.

Key capabilities highlighted in the demo include:

• Tool discovery and description – Each method is annotated with a clear, human‑readable description that the LLM uses for intent matching.
• Zero‑code client integration – After registering the server, any MCP‑compliant client (e.g., GitHub Copilot Chat) can list and invoke the tools with a simple UI click.
• Extensibility – While the sample exposes three string utilities, the same pattern scales to complex business logic, database queries, or external API calls.
• Local execution – All tool invocations run on the developer’s machine, preserving privacy and reducing latency compared to cloud‑only solutions.

Real‑world scenarios for this server include:

• Code review assistants that can format or lint code snippets on demand.
• Data‑validation tools that check input against business rules before committing changes.
• Custom NLP pipelines where the assistant can trigger language‑specific processing steps implemented in C#.

Integrating Mcpservertest into an AI workflow is straightforward: after building the console application, add it to VS Code’s MCP configuration, launch the server, and use any compatible client. The assistant will automatically surface the available tools in its UI, allowing users to invoke them with natural language prompts. This tight coupling eliminates the need for separate microservices or REST endpoints, accelerating prototyping and reducing operational overhead.

In summary, Mcpservertest provides a clear, educational blueprint for turning C# logic into AI‑assistant tools. Its minimal setup, intuitive attribute‑based registration, and direct integration with popular IDE chat clients make it an ideal starting point for developers looking to extend AI assistants with custom, local functionality.