Monsoft

You can extend your AI assistant with a Monsoft MCP Server that runs locally and exposes capabilities via a standard server entry point. This MCP server compiles to a distributable JavaScript file and is designed to be invoked by your AI client, enabling you to access external resources and enhanced context within conversations.

How to use

To use this MCP server with your AI assistant, run the local MCP process and point your client to the server’s entry file. The server is started by running your normal Node.js invocation that executes the built server file, and your client is configured to call that local process to access the MCP capabilities.

How to install

Prerequisites you need before installation are a supported Node.js runtime and a package manager.

1. Ensure Node.js is installed (v16 or higher). 2. Ensure npm or yarn is available.

Then follow these exact commands to install and build the MCP server locally.

# 1) Clone the repository
git clone https://github.com/Monsoft-Solutions/model-context-protocols.git
cd model-context-protocols

# 2) Install dependencies
npm install
# or
yarn install

# 3) Build the project
npm run build
# or
yarn build

Configuration and usage notes

Configure your AI client to invoke the MCP server by referencing the built server entry. Copy the full path to the desired MCP’s dist/server/index.js file and use that path in your client configuration so the client launches the MCP process and communicates with it.

If you maintain an environment configuration, set variables as needed. The MCP server will first read environment variables from your process environment; if not found, it will fall back to an environment file. Prepare a .env file in the working directory if you need fallback values.

Example configuration (describes how to invoke the server from a client)

{
  "mcpServers": {
    "monsoft_mcp_name": {
      "command": "node",
      "args": ["/full/path/to/model-context-protocols/mcp-name/dist/server/index.js"]
    }
  }
}

Notes on contributing and running

If you contribute improvements, follow standard collaboration practices: create a feature branch, implement changes, run tests, and submit a pull request. Ensure your changes are compatible with the MCP server workflow and that you update any related tests as needed.

Security and troubleshooting

Handle secrets and API keys with care. Do not commit sensitive information to version control. If you encounter startup errors, verify that the entry file path is correct, the build completed successfully, and your client is correctly invoking the local MCP process.