System Requirements
Relevant source files
The following files were used as context for generating this wiki page:
The mcpmcp-server is designed to facilitate the discovery, setup, and integration of Model Context Protocol (MCP) servers with various AI clients. It acts as a bridge, allowing users to unlock AI potential within their daily workflows by providing a standardized interface for remote MCP management.
System requirements for this project encompass the runtime environment, network connectivity for remote server communication, and specific configuration formats required by host AI applications like Claude Desktop.
Sources: README.md:1-5
Runtime Environment Requirements
The project is built using TypeScript and Node.js technologies. To execute the server or integrate it via the provided command-line instructions, the following environment components are required:
Node.js and NPM
The system relies on npx for execution, which necessitates a modern installation of Node.js and the Node Package Manager (npm). Specifically, the usage of npx -y mcp-remote@latest implies the environment must support fetching and executing the latest version of the mcp-remote package dynamically.
Sources: README.md:11-13, package.json
Development Dependencies
For contributors or users building from source, the project requires the TypeScript compiler and specific configuration definitions.
- TypeScript: Used for transpiling source code.
- Target Engine: The project is configured to target
ES2022or higher, as defined in the compiler options.
Sources: tsconfig.json:4-10, package.json
Network and Connectivity Requirements
The mcpmcp-server functions as a remote-capable service. It requires active network access to communicate with its centralized discovery and management endpoint.
Remote Endpoint Access
The server configuration points to https://mcpmcp.io/mcp. The system requires:
- Outbound HTTPS access to
mcpmcp.io. - Support for the Model Context Protocol over the specified network transport (typically stdio or SSE).
Sources: README.md:13, src/index.ts
Communication Flow
The following diagram illustrates the connectivity flow between the user's local client and the remote MCP infrastructure:
The diagram shows how the local client uses npx to bridge to the remote mcpmcp.io endpoint to retrieve tools and resources.
Sources: README.md:11-15
Client Configuration Requirements
Integrating the mcpmcp-server into a local client requires specific JSON configuration structures.
Configuration Schema
Clients must support the mcpServers configuration key. The entry for mcpmcp must define the command and arguments as follows:
| Parameter | Value | Description |
|---|---|---|
command | npx | The executable used to run the bridge. |
args | ["-y", "mcp-remote@latest", "https://mcpmcp.io/mcp"] | Arguments to fetch and point to the remote server. |
Sources: README.md:10-16
Platform Compatibility
- macOS: Verified compatibility with Claude Desktop on macOS.
- Other Platforms: May require variations in configuration depending on how the host application manages environment variables and shell execution.
Sources: README.md:19-20
Project Structure and Build Requirements
The project follows a standard TypeScript structure, which dictates how the system is compiled and prepared for deployment.
| Directory/File | Purpose |
|---|---|
src/ | Contains the TypeScript source files (index.ts, types.ts). |
tsconfig.json | Defines compilation rules (Module: ESNext, Target: ES2022). |
package.json | Manages dependencies and package metadata. |
Sources: tsconfig.json, package.json, src/index.ts
Type System Requirements
The project uses internal type definitions to maintain protocol consistency. Development and execution rely on these types for handling MCP messages and server state.
The relationship between the local configuration and the established server connection.
Sources: src/types.ts, README.md:10-15
Conclusion
The mcpmcp-server system requirements focus on a Node.js-capable environment with reliable internet access to mcpmcp.io. By utilizing standard tools like npx and following the Model Context Protocol, it ensures a lightweight footprint on the user's system while providing robust access to remote AI tools.