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/sdkConfiguration
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
- executeCustom 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/callWith 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
- Tool Design - Keep tools focused and single-purpose
- Error Messages - Provide clear, actionable errors
- Documentation - Include examples in tool descriptions
- Rate Limiting - Implement appropriate limits
- Security - Validate all inputs from AI models
- Monitoring - Track tool usage and errors