Allows the MCP server to be used as a tool within Langflow, enabling Langflow flows to invoke a Watson Orchestrate agent.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@wxo-agent-mcp Use invoke_agent to ask: What is the weather in Amsterdam?"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Simple MCP (Model Context Protocol) server that invokes a single Watson Orchestrate agent remotely. The agent is defined once via environment variables or MCP config.
Use this when you want a lightweight MCP that only chats with one agent—no tool management, no agent listing, no flows. Just invoke_agent(message) and get_agent().
Full documentation: DOCUMENTATION.md (includes screenshots for VS Code and Langflow).
Publishing: PUBLISHING.md – npm and MCP Registry procedure.
┌───────────────────────────────────────┐
│ Cursor • VS Code • Langflow • etc. │
│ (MCP clients) │
└──────────────────┬───────────────────┘
│ stdio / JSON-RPC
▼
┌───────────────────────────────────────┐
│ wxo-agent-mcp │
│ invoke_agent • get_agent │
└──────────────────┬───────────────────┘
│ HTTP / REST
▼
┌───────────────────────────────────────┐
│ Watson Orchestrate │
│ agent + tools + LLM │
└───────────────────────────────────────┘Tool | Description |
invoke_agent | Send a message to the configured Watson Orchestrate agent. The agent responds using its tools and LLM. |
get_agent | Get details of the configured agent (name, description, tools, instructions). |
Related MCP server: Azure AI Foundry Agent MCP
Set these in .env or your MCP client config (e.g. Cursor mcp.json):
WO_API_KEY=your-ibm-cloud-api-key
WO_INSTANCE_URL=https://your-instance-id.orchestrate.ibm.com
WO_AGENT_ID=your-agent-id
# Or WO_AGENT_IDs=id1,id2 (first is used)npm install
npm run build
cp .env.example .env
# Edit .env with your credentials and agent ID
WO_API_KEY=... WO_INSTANCE_URL=... WO_AGENT_ID=... node dist/index.js# Default question ("Hello, who are you?")
WO_API_KEY=... WO_INSTANCE_URL=... WO_AGENT_IDs=... npm run test:verify
# Custom question
npm run test:verify -- -ask "What is the weather in Amsterdam?"Runs get_agent and invoke_agent to confirm connectivity.
Open the wxo-agent-mcp folder in VS Code.
Run npm run build.
.vscode/mcp.json registers the MCP server as wxo-agent and loads .env.
Open Copilot Chat (Ctrl+Shift+I) and ask: Use invoke_agent to ask: What is the weather in Amsterdam?
For "what can you do", use Use invoke_agent to ask: What can you do? to avoid Copilot calling get_agent first.
Or run npm run test:verify from the terminal (Ctrl+`).
See DOCUMENTATION.md for more prompts and setup.
Question examples: Full list →
invoke_agent | get_agent |
What is the weather in Amsterdam? | Use get_agent to show agent details |
Tell me a dad joke | What tools does my agent have? |
What time is it in Tokyo? | |
What can you help me with? |
Langflow: Add an MCP Tools component, choose STDIO, set Command=node, Args=["/path/to/dist/index.js"], and env vars. See DOCUMENTATION.md.
Add to Cursor — Opens the Cursor Directory page. After adding, set WO_API_KEY, WO_INSTANCE_URL, and WO_AGENT_ID in Cursor MCP settings.
For cursor.directory/mcp/new submission:
Field | Value |
Cursor Deep Link |
|
Install instructions | https://github.com/markusvankempen/wxo-agent-mcp#mcp-client-configuration |
.cursor/mcp.json){
"mcpServers": {
"wxo-agent": {
"command": "npx",
"args": ["-y", "wxo-agent-mcp"],
"env": {
"WO_API_KEY": "your-api-key",
"WO_INSTANCE_URL": "https://xxx.orchestrate.ibm.com",
"WO_AGENT_ID": "your-agent-id"
}
}
}
}.vscode/mcp.json)The server is named wxo-agent. Use envFile to load .env:
{
"servers": {
"wxo-agent": {
"type": "stdio",
"command": "node",
"args": ["${workspaceFolder}/dist/index.js"],
"envFile": "${workspaceFolder}/.env"
}
}
}For npx (after publishing), use "command": "npx", "args": ["-y", "wxo-agent-mcp"], and add env with your credentials.
Same config as Cursor. Copy examples/antigravity-mcp.json or add to your MCP config:
{
"mcpServers": {
"wxo-agent": {
"command": "npx",
"args": ["-y", "wxo-agent-mcp"],
"env": {
"WO_API_KEY": "your-api-key",
"WO_INSTANCE_URL": "https://xxx.orchestrate.ibm.com",
"WO_AGENT_ID": "your-agent-id"
}
}
}
}wxo-agent-mcp | wxo-builder-mcp-server | |
Purpose | Invoke one agent | Full dev toolkit (tools, agents, connections, flows) |
Agent | Single | Multiple agents, |
Tools |
| 30+ tools (list_skills, deploy_tool, etc.) |
Use case | Chat with a specific agent | Build and manage Watson Orchestrate resources |
npm: wxo-agent-mcp
MCP Registry: io.github.markusvankempen/wxo-agent-mcp
GitHub: markusvankempen/wxo-agent-mcp
Apache-2.0
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