Learn about MCP demo with client and server for studying MCP concepts
mcp-demo is an open-source implementation designed to facilitate the establishment and maintenance of Model Context Protocol (MCP) servers for developers building advanced AI applications. The server acts as a universal adapter, enabling various AI tools—such as Claude Desktop, Continue, Cursor, and others—to connect to specific data sources and tools through a standardized protocol. By leveraging mcp-demo, developers can enhance the flexibility and interoperability of their AI workflows, ensuring consistent performance and reliability across different environments.
MCP servers are essential components in modern AI application ecosystems, providing a secure and efficient framework for accessing diverse data sources and tools. Through its core features, mcp-demo offers robust support for various AI applications, ensuring seamless integration and improved operational efficiency:
The architecture of the mcp-demo server is designed to facilitate seamless integration with various AI tools and platforms. The core components include:
The following Mermaid diagram highlights the flow of interactions between an MCP client and the mcp-demo server:
graph TD;
A[AI Application] -->|MCP Client| B[MCP Protocol]
B --> C[MCP Server];
C --> D[Data Source/Tool];
style A fill:#e1f5fe;
style C fill:#f3e5f5;
style D fill:#e8f5e8;
To get started with mcp-demo, follow these steps to install and run the server:
Clone the Repository:
git clone https://github.com/modelcontext/mcp-demo.git
cd mcp-demo
Install Dependencies:
npm install
Run the Server:
npm start
mcp-demo serves as a pivotal component for developers looking to integrate diverse data sources and tools into their AI workflows. Here are two realistic use cases that demonstrate its versatile capabilities:
In this scenario, an AI application like Continue uses mcp-demo to connect to real-time financial market data from a specific API. By leveraging the standardized protocol, the server ensures secure and efficient communication between the AI tool and the data source.
graph TD;
A[Continue] -->|MCP Client| B[MCP Protocol]
B --> C[MCP Server];
C --> D[Financial Market Data API];
In this use case, an AI application like Cursor uses mcp-demo to integrate with external productivity tools such as Jira and Trello. This integration allows users to automate tasks, such as updating project statuses based on AI-generated insights.
graph TD;
A[Cursor] -->|MCP Client| B[MCP Protocol]
B --> C[MCP Server];
C --> D[Jira/Trello API];
mcp-demo supports a range of key MCP clients, ensuring broad compatibility and ease of integration:
The following table provides an overview of MCP client compatibility:
MCP Client | Resources | Tools | Prompts | Status |
---|---|---|---|---|
Claude Desktop | ✅ | ✅ | ✅ | Full Support |
Continue | ✅ | ✅ | ✅ | Full Support |
Cursor | ❌ | ✅ | ❌ | Tools Only |
mcp-demo has been rigorously tested to ensure optimal performance and compatibility across a wide range of environments. The following matrix provides details on the server's performance characteristics:
mcp-demo offers advanced configuration options to tailor the server's behavior to specific use cases. Here’s a sample configuration snippet:
{
"mcpServers": {
"[server-name]": {
"command": "npx",
"args": ["-y", "@modelcontextprotocol/server-[name]"],
"env": {
"API_KEY": "your-api-key"
}
}
}
}
Q: Can the mcp-demo server connect to multiple data sources?
A: Yes, mcp-dem supports connecting to multiple data sources and tools simultaneously, ensuring a robust and flexible environment for AI applications.
Q: What are the minimum system requirements for running the mcp-demo server?
A: The recommended minimum system requirements include at least 4 GB of RAM and 100 MB of available disk space on your machine.
Q: Is there any cost associated with using the mcp-demo server?
A: mcp-dem is an open-source project, so it is free to use without any associated costs.
Q: How does the mcp-demo server ensure data privacy and security?
A: The server employs robust encryption methods and secure API key management to protect sensitive data during transmission and storage.
Q: Can I customize the MCP protocol for my specific needs?
A: Yes, mcp-dem is highly customizable, allowing developers to modify the protocol according to their specific requirements.
Contributions to the mcp-demo repository are welcome and greatly appreciated. To get started with contributing, follow these guidelines:
Fork the Repository:
git fork https://github.com/modelcontext/mcp-demo.git
Clone Your Fork:
git clone https://github.com/yourusername/mcp-demo.git
cd mcp-demo
Create a New Feature Branch:
git checkout -b feature-branch-name
Commit Changes and Push to Remote Repository:
git add .
git commit -m "Your detailed description of the change"
git push origin feature-branch-name
Open a Pull Request:
For more information on the MCP ecosystem, check out these resources:
By leveraging mcp-demo, developers can build robust and scalable AI solutions that seamlessly integrate with a variety of tools and data sources. Dive into the world of MCP integration today and unlock new possibilities in the development of AI applications.
The provided documentation aims to position mcp-demo as a valuable tool for developers working on advanced AI application projects, emphasizing its core features, real-world use cases, and extensive compatibility options.
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