Overview of the Sample MCP Server

The Sample MCP Server serves as a lightweight, automated entry point for developers who want to expose simple resources and tools through the Model Context Protocol (MCP). It is designed to illustrate how a minimal server can be created, registered, and consumed by AI assistants such as Claude. By providing a ready‑made example, the repository helps teams understand the core concepts of MCP—resources, tools, prompts, and sampling—without the overhead of building everything from scratch.

Problem Solved

In many AI‑powered workflows, developers struggle to connect their custom data sources or utility functions to a language model in a standardized way. Existing solutions often require manual configuration of REST endpoints, OAuth flows, or custom SDKs that are hard to maintain. The Sample MCP Server removes this friction by offering a turnkey implementation of the MCP specification, allowing developers to focus on business logic rather than protocol plumbing. It demonstrates how a simple HTTP server can expose structured APIs that AI assistants can query, execute, and iterate over.

What the Server Does

The server exposes a small set of endpoints that conform to MCP’s resource and tool definitions. It hosts:

• Resources: Structured data objects (e.g., a list of books, user profiles) that can be queried or fetched by the AI assistant.
• Tools: Executable functions (e.g., data transformation, calculation) that the assistant can invoke to perform tasks on behalf of the user.
• Prompts: Pre‑defined prompt templates that guide the assistant’s responses, ensuring consistent tone and formatting.
• Sampling: Configuration for text generation sampling (temperature, top‑k) that tailors the assistant’s output to specific use cases.

By packaging these elements together, the server provides a cohesive API surface that AI assistants can discover and interact with dynamically. The server’s architecture encourages extensibility; developers can add new resources or tools simply by defining additional JSON schemas and handler functions.

Key Features Explained

• Protocol‑compliant API: Adheres strictly to the MCP specification, ensuring seamless discovery by any MCP‑aware client.
• Declarative configuration: Resources and tools are defined through JSON/YAML, making the server easy to modify without code changes.
• Real‑time execution: Tools are executed on demand, allowing the assistant to perform calculations or data lookups instantly.
• Custom sampling control: Fine‑tune generation parameters per endpoint, enabling consistent and predictable assistant behavior.
• Self‑documenting: The server exposes introspection endpoints that return schema definitions, allowing clients to auto‑generate UI components or validation logic.

Use Cases and Real‑World Scenarios

• Data retrieval for conversational agents: A customer support bot can query the server’s resources to fetch product details or order status in real time.
• Dynamic content generation: A marketing assistant can invoke sampling tools to produce variations of ad copy while maintaining brand guidelines.
• Business logic execution: An internal workflow tool can expose calculation utilities (e.g., tax, discount) that the assistant calls to assist users with financial queries.
• Rapid prototyping: Developers can spin up the Sample MCP Server to quickly test how an AI assistant interacts with custom data before moving to production.

Integration into AI Workflows

The server is designed to be discovered automatically by MCP clients. Once integrated, an assistant can:

1. Discover available resources and tools via introspection endpoints.
2. Invoke a tool by sending a structured request that includes the tool name and parameters.
3. Retrieve resource data or receive computed results, which can be embedded directly into the assistant’s response.
4. Iterate by adjusting sampling parameters or chaining multiple tool calls to refine output.

Because the server follows MCP’s standard, it plugs into any existing AI orchestration framework that supports the protocol—whether it’s a custom-built pipeline, a platform like Anthropic or OpenAI, or an in‑house solution.

Unique Advantages

• Zero boilerplate: The repository contains all the necessary scaffolding to get a compliant MCP server running immediately.
• Extensibility: Adding new tools or resources is as simple as editing a JSON schema and writing a handler, making it ideal for rapid feature iteration.
• Open‑source clarity: The codebase is intentionally minimal and well‑commented, providing a clear learning path for developers new to MCP.
• Community friendly: By following the official MCP spec, the server can be extended or forked by any organization without compatibility concerns.

In summary, the Sample MCP Server is a practical showcase of how to expose structured data and executable utilities to AI assistants via