.do
Mcp

Integrations MCP

Model Context Protocol reference for integrations.do - Connect external Application Programming Interface (API)s and systems to business processes

Integrations MCP

Connect external Application Programming Interface (API)s and systems to business processes

Overview

The Model Context Protocol (MCP) provides AI models with direct access to integrations.do through a standardized interface.

Installation

pnpm add @modelcontextprotocol/sdk

Configuration

Add to your MCP server configuration:

{
  "mcpServers": {
    "integrations": {
      "command": "npx",
      "args": ["-y", "@dotdo/mcp-server"],
      "env": {
        "DO_API_KEY": "your-api-key"
      }
    }
  }
}

Tools

integrations/invoke

Main tool for integrations.do operations.

{
  "name": "integrations/invoke",
  "description": "Connect external Application Programming Interface (API)s and systems to business processes",
  "inputSchema": {
    "type": "object",
    "properties": {
      "operation": {
        "type": "string",
        "description": "Operation to perform"
      },
      "parameters": {
        "type": "object",
        "description": "Operation parameters"
      }
    },
    "required": ["operation"]
  }
}

Usage in AI Models

Claude Desktop

// ~/Library/Application Support/Claude/claude_desktop_config.json
{
  "mcpServers": {
    "integrations": {
      "command": "npx",
      "args": ["-y", "@dotdo/mcp-server", "--tool=integrations"],
      "env": {
        "DO_API_KEY": "undefined"
      }
    }
  }
}

OpenAI GPTs

# Custom GPT configuration
tools:
  - type: mcp
    server: integrations
    operations:
      - invoke
      - query
      - execute

Custom Integration

import { Client } from '@modelcontextprotocol/sdk/client/index.js'
import { StdioClientTransport } from '@modelcontextprotocol/sdk/client/stdio.js'

const transport = new StdioClientTransport({
  command: 'npx',
  args: ['-y', '@dotdo/mcp-server', '--tool=integrations'],
})

const client = new Client(
  {
    name: 'integrations-client',
    version: '1.0.0',
  },
  {
    capabilities: {},
  }
)

await client.connect(transport)

// Call tool
const result = await client.callTool({
  name: 'integrations/invoke',
  arguments: {
    operation: 'integrations',
    parameters: {},
  },
})

Tool Definitions

Available Tools

{
  "tools": [
    {
      "name": "integrations/invoke",
      "description": "Invoke integrations.do",
      "inputSchema": {
        /* ... */
      }
    },
    {
      "name": "integrations/query",
      "description": "Query integrations.do resources",
      "inputSchema": {
        /* ... */
      }
    },
    {
      "name": "integrations/status",
      "description": "Check integrations.do status",
      "inputSchema": {
        /* ... */
      }
    }
  ]
}

Resources

Available Resources

{
  "resources": [
    {
      "uri": "integrations://config",
      "name": "Integrations Configuration",
      "mimeType": "application/json"
    },
    {
      "uri": "integrations://docs",
      "name": "Integrations Documentation",
      "mimeType": "text/markdown"
    }
  ]
}

Prompts

Pre-configured Prompts

{
  "prompts": [
    {
      "name": "integrations-quick-start",
      "description": "Quick start guide for integrations.do",
      "arguments": []
    },
    {
      "name": "integrations-best-practices",
      "description": "Best practices for integrations.do",
      "arguments": []
    }
  ]
}

Examples

Basic Usage

// AI model calls tool via MCP
mcp call integrations/call

With Parameters

// Call with parameters
await mcp.callTool('integrations/invoke', {
  operation: 'process',
  parameters: {
    // Operation-specific parameters
  },
  options: {
    timeout: 30000,
  },
})

Error Handling

try {
  const result = await mcp.callTool('integrations/invoke', {
    operation: 'process',
  })
  return result
} catch (error) {
  if (error.code === 'TOOL_NOT_FOUND') {
    console.error('Integrations tool not available')
  } else {
    throw error
  }
}

AI Integration Patterns

Agentic Workflows

// AI agent uses integrations.do in workflow
const workflow = {
  steps: [
    {
      tool: 'integrations/invoke',
      operation: 'analyze',
      input: 'user-data',
    },
    {
      tool: 'integrations/process',
      operation: 'transform',
      input: 'analysis-result',
    },
  ],
}

Chain of Thought

AI models can reason about integrations.do operations:

User: "I need to process this data"

AI: "I'll use the integrations tool to:
1. Validate the data format
2. Process it through integrations.do
3. Return the results

Let me start..."

[Calls: mcp call integrations/call]

Server Implementation

Custom MCP Server

import { Server } from '@modelcontextprotocol/sdk/server/index.js'
import { StdioServerTransport } from '@modelcontextprotocol/sdk/server/stdio.js'

const server = new Server(
  {
    name: 'integrations-server',
    version: '1.0.0',
  },
  {
    capabilities: {
      tools: {},
      resources: {},
      prompts: {},
    },
  }
)

// Register tool
server.setRequestHandler('tools/call', async (request) => {
  if (request.params.name === 'integrations/invoke') {
    // Handle integrations.do operation
    return {
      content: [
        {
          type: 'text',
          text: JSON.stringify(result),
        },
      ],
    }
  }
})

const transport = new StdioServerTransport()
await server.connect(transport)

Best Practices

  1. Tool Design - Keep tools focused and single-purpose
  2. Error Messages - Provide clear, actionable errors
  3. Documentation - Include examples in tool descriptions
  4. Rate Limiting - Implement appropriate limits
  5. Security - Validate all inputs from AI models
  6. Monitoring - Track tool usage and errors