Model Context Protocol MCP With Vercel Functions

What is model-context-protocol-mcp-with-vercel-functions?

model-context-protocol-mcp-with-vercel-functions is a server setup designed by chinpeerapat to leverage the Model Context Protocol (MCP) on Vercel's infrastructure. This setup enhances AI capabilities by securely connecting them to various data sources and tools. The Model Context Protocol is an open-source protocol that facilitates AI systems, like Anthropic's Claude, to access external data, tools, and prompts seamlessly. By running on Vercel, the server benefits from the platform's dynamic and scalable cloud functions, ensuring efficient and secure operations.

How to Use model-context-protocol-mcp-with-vercel-functions

To utilize the model-context-protocol-mcp-with-vercel-functions, follow these steps:

1. Setup the Server: Begin by updating the api/server.ts file with your desired tools, prompts, and resources. This setup is crucial for customizing the server to meet your specific AI integration needs.

2. Configure Vercel: Ensure that your Vercel project has a Redis instance attached, as this is necessary for managing data efficiently. Set the process.env.REDIS_URL to point to your Redis instance.

3. Enable Fluid Compute: For optimal performance, enable Fluid Compute in your Vercel account. This feature allows for more efficient execution of server functions. If you're on a Vercel Pro or Enterprise plan, adjust the vercel.json file to set the max duration to 800 to accommodate longer processing times.

4. Deploy the Template: Deploy the MCP template on Vercel to get your server up and running. This step is crucial for ensuring that all configurations are correctly applied.

5. Test with Sample Client: Use the provided sample client script, script/test-client.mjs, to test the server's functionality. Run the script using Node.js to initiate test invocations and verify the server's operation.

Key Features of model-context-protocol-mcp-with-vercel-functions

• Secure Data Access: The server ensures secure connections between AI systems and various data sources, including files, databases, and APIs, using the Model Context Protocol.

• Customizable Integration: Users can tailor the server setup by updating the api/server.ts file, allowing for specific tools, prompts, and resources to be integrated as needed.

• Scalable Infrastructure: Hosted on Vercel, the server benefits from a scalable and dynamic cloud environment, supporting efficient execution and deployment.

• Redis Support: Integration with Redis provides robust data management capabilities, essential for handling real-time data access and storage.

• Fluid Compute: By enabling Fluid Compute, the server can handle complex computations more effectively, ensuring smooth operation even under demanding conditions.

• Easy Deployment: With the MCP template, deploying the server is straightforward, allowing users to get started quickly without extensive configuration.

• Testing and Validation: The inclusion of a sample client script facilitates easy testing and validation of server functionality, ensuring that all integrations work as expected before going live.

This setup empowers AI systems to enhance their data handling capabilities, providing a secure and efficient framework for expanding AI functionalities on Vercel's cutting-edge infrastructure.