mcp

This MCP server implements the Model Context Protocol with support for both Wasm Component Model backends and a native backend, enabling running MCP servers via a standard transport (Stdio). It exposes capabilities for providing tools and prompts to AI models, and can handle client calls such as listing roots when available. Use this server to deploy a configurable MCP runtime that can be driven by your tooling or CI pipelines and integrated into larger AI workflows. The server is designed to work with the Wasm and native backends described in the repository, and can be extended to support additional implementations that conform to the MCP interface.

Prerequisites:

• Node.js (LTS version) or a compatible runtime if using the Node-based entrypoint
• Git
• Access to the repository (clone or download the package)

Install steps:

1. Clone the repository: git clone https://github.com/peter-jerry-ye/mcp.git cd mcp

2. Install dependencies (if using the Node-based setup): npm install

3. Build or prepare backend components as required by the repository (check any provided build scripts or example backends).

4. Start the MCP server (adjust the command as needed based on your environment): node path/to/server.js

5. Verify startup by connecting a client or using any provided example clients to exercise the Tools and Prompts features.

Notes:

• This MCP server is designed to work with multiple backends (Wasm Component Model and native). Ensure the chosen backend is properly built and accessible at runtime.
• Transport is via Stdio, so the server expects standard input/output streams from the client. When integrating into containers or orchestrators, make sure the I/O streams are correctly wired.
• Features include Tools and Prompts for providing capabilities to models, and the ability to List roots if the client provides the corresponding capabilities.
• If you plan to run multiple instances, consider unique server identifiers and separate I/O streams to avoid cross-talk between instances.
• Check for environment variable requirements in your deployment environment and set them in the process environment before starting the server.