Overview

The Tutorial MCP Server Dotnet is a lightweight, example implementation of the Model Context Protocol (MCP) built with .NET and C#. It demonstrates how an AI assistant can expose a set of resources, tools, prompts, and sampling capabilities to external clients through a standardized HTTP interface. By following this tutorial, developers can quickly grasp the core concepts of MCP and use the codebase as a foundation for building more sophisticated, domain‑specific servers.

Solving the Integration Gap

Many AI assistants rely on external data or specialized computation that resides outside of their native environment. Without a clear contract, integrating these services can be error‑prone and difficult to maintain. The MCP server solves this problem by providing a single, well‑defined API surface that describes exactly what operations are available and how they should be invoked. Clients can discover capabilities at runtime, validate requests against the server’s schema, and handle responses in a consistent manner. This eliminates the need for custom adapters or hard‑coded endpoint logic.

Core Functionality and Value

At its heart, the server implements the MCP specification for resources (data endpoints), tools (executable actions), prompts (pre‑defined conversational starters), and sampling (text generation parameters). By exposing these through RESTful endpoints, the server allows AI assistants to:

• Fetch structured data (e.g., user profiles or product catalogs) via resource endpoints.
• Invoke computational tasks (e.g., image generation, data analysis) through tool calls that return JSON payloads.
• Pre‑populate dialogues with contextually relevant prompts, streamlining user interactions.
• Control generation quality and style by adjusting sampling parameters such as temperature or token limits.

This modular approach means developers can add, remove, or update capabilities without breaking existing clients. It also promotes separation of concerns, letting AI assistants focus on natural language understanding while delegating domain logic to the MCP server.

Key Features Explained

• Dynamic Capability Discovery: Clients query to retrieve a manifest of all available resources, tools, prompts, and sampling options. This enables adaptive behavior based on what the server offers.
• Typed Request/Response Contracts: Each endpoint enforces JSON schemas, ensuring that data passed between the assistant and server is validated and well‑structured.
• Extensible Tool Framework: New tools can be added by implementing a simple interface, allowing the server to execute arbitrary code or external services while keeping the API contract intact.
• Sampling Configuration: Clients can fine‑tune text generation by specifying parameters that the server forwards to the underlying model, providing control over creativity and verbosity.
• Built‑in Documentation: The server automatically generates human‑readable documentation (often via Swagger or similar), making it easier for developers to explore and test endpoints.

Real‑World Use Cases

• E‑commerce Assistants: Fetch product data, calculate discounts, and recommend items by calling resource endpoints and tools that interface with inventory systems.
• Healthcare Bots: Retrieve patient records, run diagnostic algorithms, and generate reports through secure, typed tool calls.
• Financial Advisors: Access market data, perform risk calculations, and generate investment summaries using dedicated tools.
• Education Platforms: Pull curriculum resources, run grading scripts, and provide adaptive learning prompts.

In each scenario, the MCP server acts as a bridge that keeps the AI assistant’s conversational logic separate from domain expertise, ensuring maintainability and scalability.

Integration Into AI Workflows

Developers can embed the MCP server into their existing infrastructure by:

1. Registering the server’s endpoint with the AI assistant’s configuration, enabling it to discover available capabilities.
2. Mapping tool calls in the assistant’s prompt templates to corresponding MCP tool endpoints, allowing dynamic execution during conversations.
3. Using sampling parameters from the server’s manifest to tailor response generation on a per‑use‑case basis.
4. Extending the server with custom resources or tools as new business requirements emerge, without redeploying the assistant.

Because MCP is language‑agnostic and relies on standard HTTP/JSON, integrating this server with a variety of AI platforms—Claude, OpenAI, or custom models—is straightforward.

Standout Advantages

• Standardization: Adheres to the MCP spec, ensuring compatibility across different assistants and tooling ecosystems.
• Rapid Prototyping: The .NET implementation provides a clean, type‑safe foundation that accelerates development of production‑ready servers.
• Extensibility: The tool framework is designed for plug‑and‑play, allowing developers to add complex logic without touching the core