openmcp-sdk : Deployment framework for openmcp

Deploy your agent from lab to production at lightning speed ⚡

Introduction & Installation

What is openmcp-sdk.js

OpenMCP Client provides an all-in-one MCP debugging solution — which is great, but not quite enough.

Often, we want to take our developed MCPs and make them distributable apps, or deploy them as function services or microservices on a server. However, since OpenMCP Client places all logic for interacting with large models and using tools on the frontend, it becomes difficult to turn an MCP into an independent, model-bound application or service.

That’s where openmcp-sdk.js comes in.
It provides a lightweight Node.js-based solution that allows you to seamlessly deploy your MCP and its debugged workflow as an agent through Node.js.

---

Installation

npm

npm install openmcp-sdk

yarn

yarn add openmcp-sdk

pnpm

pnpm add openmcp-sdk

---

Usage

First, create a new TypeScript project:

mkdir clever-agent && cd clever-agent
npm init -y
npm install typescript tsx @types/node --save-dev
tsc --init

Since openmcp-sdk only supports ESM, make sure to add "type": "module" in your package.json.

Then install openmcp-sdk:

npm install openmcp-sdk

Now, create a file named main.ts. With just a few lines of code, you can deploy your configuration as an agent:

import { OmAgent } from 'openmcp-sdk/service/sdk';

const agent = new OmAgent();

// Load the configuration — it can be automatically generated by OpenMCP Client after debugging
agent.loadMcpConfig('./mcpconfig.json');

// Load the pre-debugged prompt
const prompt = await agent.getPrompt('hacknews', { topn: '5' });

// Execute the task
const res = await agent.ainvoke({ messages: prompt });

console.log('⚙️ Agent Response', res);

The mcpconfig.json can be directly exported from openmcp-client — no need to write it manually. Here’s an example:

{
    "version": "1.0.0",
    "namespace": "openmcp",
    "mcpServers": {
        "my-browser": {
            "command": "mcp",
            "args": [
                "run",
                "~/projects/openmcp-tutorial/crawl4ai-mcp/main.py"
            ],
            "description": "A MCP for long-term memory support",
            "prompts": [
                "hacknews"
            ]
        }
    },
    "defaultLLM": {
        "baseURL": "https://api.deepseek.com",
        "apiToken": "sk-xxxxxxxxxxxxxx",
        "model": "deepseek-chat"
    }
}

Run it with:

npx tsx main.ts

And you’ll see output like this:

2025/6/20 - 20:47:31 | 🚀 [crawl4ai-mcp] 1.9.1 connected, type STDIO
2025/6/20 - 20:47:35 | 🤖 Agent wants to use these tools get_web_markdown
2025/6/20 - 20:47:39 | ✅  get_web_markdown success
2025/6/20 - 20:47:46 | 🤖 Agent wants to use tools(3) get_web_markdown, get_web_markdown, get_web_markdown
2025/6/20 - 20:47:48 | ✅  get_web_markdown success
2025/6/20 - 20:47:54 | ✅  get_web_markdown success
2025/6/20 - 20:47:57 | ✅  get_web_markdown success
                        └─ 📥145434 📤1554 🧮96.1% 💰0.0439 rmb

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