MCP Server Sample: Model Context Protocol Integration

Overview: What is MCP Server?

This repository contains an implementation of a Model Context Protocol (MCP) server for educational purposes. The MCP protocol standardizes how applications provide context to Language Models (LMs), such as Large Language Models (LLMs). Think of MCP like a USB-C port for AI applications, providing a standardized way to connect LLMs to different data sources and tools. This documentation will guide you through setting up a functional MCP server that can integrate with various LLM clients.

🔧 Core Features & MCP Capabilities

The core capabilities of the MCP server include:

• Resource Provisioning: File-like data (e.g., API responses, file contents) accessible by MCP clients.
• Tool Execution: Functions callable by the LLM with user approval (e.g., execution of scripts, API calls).
• Prompt Templates: Pre-written templates to help users accomplish specific tasks.

These capabilities enhance the interoperability and flexibility of AI applications. By adhering to this protocol, developers can ensure seamless integration across a wide range of platforms and tools.

⚙️ MCP Architecture & Protocol Implementation

MCP follows a client-server architecture where hosts (like Claude Desktop, IDEs, or AI tools) connect to servers through the standardized protocol:

Diagram: MCP Protocol Flow

graph TD
    A[AI Application] -->|MCP Client| B[MCP Protocol]
    B --> C[MCP Server]
    C --> D[Data Source/Tool]
    style A fill:#e1f5fe
    style B fill:#a8def0
    style C fill:#f3e5f5
    style D fill:#e8f5e8

Diagram: Data Architecture

graph LR
    S[AI Application] -->|Request| R[Resource Service]
    R -->|Data Fetch| D[Database/Disk Storage]
    T[Tool Execution Service] -->|Action| E[Execution Outcome]
    S -->|Command| T
    F[Prompt Template Service] -->|Output| P[Dynamic Prompt Output]
    style S fill:#d3deea
    style R fill:#bbdefb
    style D fill:#ccf2ff
    style T fill:#c8e6c9
    style E fill:#f0f4c3
    style F fill:#fff176

🚀 Getting Started with Installation

To get started, follow these steps:

Setup Project Environment

Using the uv package manager:

uv init mcp-server-sample
cd mcp-server-sample
uv add "mcp[cli]"

Alternatively, for projects using pip:

pip install "mcp[cli]"

Running Standalone Development Tools

To run the MCP command with uv:

uv run mcp

💡 Key Use Cases in AI Workflows

AI workflows often require dynamic resource access and tool execution. Here are two real-world use cases demonstrating how this server can be integrated:

1. Financial Analysis: An LLM could request historical stock data from an API (resource) and use a financial analysis tool to predict future trends.
2. Code Generation: A text-based request triggers code generation tools (MCP tools) within an IDE, with the generated code stored locally or transmitted to a remote service.

🔌 Integration with MCP Clients

The MCP server is compatible with several AI clients:

• Claude Desktop - Full Support
• Continue - Full Support
• Cursor - Limited Tool Support Only (no resource or prompt integration)

📊 Performance & Compatibility Matrix

This matrix provides an overview of client compatibility and support:

MCP Client	Resources	Tools	Prompts
Claude Desktop	✅	✅	✅
Continue	✅	✅	✅
Cursor	❌	✅	❌

🛠️ Advanced Configuration & Security

For advanced configuration and security best practices, consult the official MCP documentation or use the following configuration sample:

{
  "mcpServers": {
    "[server-name]": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-[name]"],
      "env": {
        "API_KEY": "your-api-key"
      }
    }
  }
}

❓ Frequently Asked Questions (FAQ)

1. How do I configure the MCP server for production use? Configure environment variables, resource paths, and tool execution permissions.

2. What are the security best practices when using MCP? Use API keys, implement rate limiting, and ensure sensitive data is encrypted.

3. How can I integrate custom resources into my MCP server? Create custom resource handlers using the MCP toolkit or framework (e.g., using mcp.server).

4. Can multiple clients connect to the same MCP server simultaneously? Yes, MCP servers support concurrent connections from multiple clients.

5. Are all LLMs compatible with MCP servers? The compatibility varies; check specific LLM documentation for MCP support.

👨‍💻 Development & Contribution Guidelines

Contributions are welcome! If you wish to contribute, kindly follow these guidelines:

1. Fork the repository and create a new branch.
2. Implement your changes and submit a pull request.
3. Ensure all tests pass before submitting.

Check the CONTRIBUTING.md file for more details on coding standards and testing practices.

🌐 MCP Ecosystem & Resources

Explore the broader MCP ecosystem by visiting the official MCP documentation or related Python packages.

For additional resources, review the GitHub repository's README for more examples and detailed usage instructions.

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This comprehensive documentation highlights key features, architecture, installation procedures, and integration strategies for developers working with AI applications. By following these guidelines, you can enhance your project's interoperability and flexibility through MCP server integration.