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Public MCP servers provide ready-made endpoints that implement the Model Context Protocol (MCP) for testing, learning, and debugging client implementations. You can connect your MCP client to any of the listed servers and interact with their /mcp endpoint to initialize sessions, call tools, and observe responses. The Echo server is particularly useful for debugging requests because it returns the incoming request data, while the Inspector service helps visualize the full MCP message flow in real time. If you’re developing an MCP client, start by initializing a session with the echo or inspector server, then practice invoking tools like echo or other available tools via the client’s tool-calling capability. The TypeScript SDK example in the README shows how to initiate a connection and call a tool against one of these public servers; you can adapt that pattern to your preferred language and tooling.

Prerequisites:

• Docker (recommended for running the public server images locally)
• curl or a HTTP client for testing MCP requests

Installation steps (Docker-based):

1. Ensure Docker is installed and running.

2. Start any of the public MCP server containers. For example, to run the Inspector server:

   docker run -d --name kite-mcp-inspector -p 8080:80 kite-mcp-inspector:latest

   Note: The README endpoints are public; you can also run equivalent containers locally if images are available in your registry.

3. Verify the server is reachable at the mapped port and /mcp endpoint, e.g. http://localhost:8080/mcp

4. Use curl or your MCP client to interact with the server, e.g. initializing a session and invoking tools as shown in the README.

If you prefer not to use Docker, you can adapt the approach to your environment once you have equivalent server images or deployments available from the project maintainers.

Tips and caveats:

• All listed servers expose MCP endpoints at /mcp and accept POST requests with MCP-compliant JSON payloads.
• The public servers are intended for testing and development; avoid high-traffic or production workloads.
• When using the Echo server for debugging, you can quickly verify the shape of your requests and responses.
• The Inspector tool helps validate your client’s message flow by visualizing the MCP messages in real time.
• If you intend to deploy your own server or maintain private test environments, consider following the repository’s GitOps workflow in the Getting Started section and using the Apps specifications to render deployments via make render and make deploy.
• For local development, you can mimic the public endpoints by running equivalent container images or by building your own MCP-compliant server implementations.