Building MCP Servers for OpenClaw Agent Skills

MCP (Model Context Protocol) is an emerging standard for connecting AI agents to external tools and data sources. In the context of OpenClaw, MCP enables Agent Skills to expose their functionality to agent backends in a structured, secure, and interoperable way.

This guide walks through creating an MCP server for an OpenClaw Agent Skill, using the skill-creator Skill as a reference implementation.

What is MCP?

MCP defines a JSON-RPC-based protocol for AI agents to request access to tools, resources, and data. Instead of hard-coding integrations, agents can discover available MCP servers and invoke their methods dynamically.

An MCP server exposes:

• Resources: Data endpoints (e.g., calendar events, email threads)
• Tools: Actions (e.g., send email, create calendar event)
• Prompts: Pre-configured instructions or templates

The agent backend (e.g., Claude, GPT) receives a structured list of available tools and can generate appropriate function calls.

Why Use MCP with OpenClaw?

1. Dynamic Tool Discovery: Agents automatically know what tools are available without static configuration
2. Standardized Interfaces: Clean separation between agent logic and tool implementation
3. Security: Explicit resource access control and approval workflows
4. Interoperability: Skills work across different agent backends that support MCP

Creating an MCP Server

Let's create a basic MCP server for the calendar Skill.

1. Define Your Resources and Tools

Start by identifying what your Skill can provide:

{
  "resources": [
    {
      "name": "calendar_events",
      "description": "Upcoming calendar events for the next 7 days"
    }
  ],
  "tools": [
    {
      "name": "create_calendar_event",
      "description": "Create a new calendar event with title, time, and optional description",
      "input_schema": {
        "type": "object",
        "properties": {
          "title": { "type": "string" },
          "start_time": { "type": "string", "format": "date-time" },
          "end_time": { "type": "string", "format": "date-time" },
          "description": { "type": "string" }
        },
        "required": ["title", "start_time", "end_time"]
      }
    }
  ]
}

2. Implement the MCP Server

Use the mcporter CLI to scaffold and run your MCP server:

# Install mcporter
npm install -g mcporter

# Create server
mcporter create --name openclaw-calendar --port 3001

This generates a basic server. Modify index.ts to connect to OpenClaw's calendar integration:

import { McpServer } from 'mcp-shared';
import { calendar } from 'openclaw-calendar'; // Hypothetical API

const server = new McpServer({
  name: 'openclaw-calendar',
  port: 3001
});

server.resource('calendar_events', async () => {
  const events = await calendar.getUpcoming(7);
  return events.map(e => ({
    id: e.id,
    title: e.title,
    start: e.start.toISOString(),
    end: e.end.toISOString()
  }));
});

server.tool('create_calendar_event', {
  description: 'Create a new calendar event',
  parameters: {
    type: 'object',
    properties: {
      title: { type: 'string' },
      start_time: { type: 'string', format: 'date-time' },
      end_time: { type: 'string', format: 'date-time' },
      description: { type: 'string' }
    },
    required: ['title', 'start_time', 'end_time']
  }
}, async (params) => {
  await calendar.create({
    title: params.title,
    start: new Date(params.start_time),
    end: new Date(params.end_time),
    description: params.description
  });
  
  return { success: true };
});

await server.start();
console.log('MCP server running on http://localhost:3001');

3. Register with OpenClaw

Add your MCP server to OpenClaw's configuration:

{
  "mcp": {
    "servers": [
      {
        "name": "calendar",
        "url": "http://localhost:3001"
      }
    ]
  }
}

Or use the CLI:

openclaw mcp add calendar http://localhost:3001

4. Test the Integration

Use mcporter to inspect your server:

mcporter list http://localhost:3001

You should see your resource and tool listed. Now when an agent in OpenClaw requests calendar access, it will automatically detect and use your MCP server.

Best Practices

• Error Handling: Return meaningful error messages from tools
• Rate Limiting: Protect against abusive agent behavior
• Authentication: For production servers, implement auth (MCP supports bearer tokens)
• Caching: Cache resource responses when appropriate to reduce load
• Logging: Log tool invocations for debugging and security auditing

Conclusion

MCP unlocks powerful, dynamic integrations between AI agents and external tools. By implementing MCP servers for your Agent Skills, you make them more discoverable, secure, and interoperable.

The OpenClaw ecosystem is moving toward MCP as the standard for skill integration, enabling richer agent capabilities while maintaining security and control.

Start by exposing one tool from your Skill via MCP, test it thoroughly, then expand to cover your Skill's full functionality.