OpenAI API + FlowMattic MCP Server Integration Guide

Overview

This guide demonstrates how to integrate FlowMattic MCP (Model Context Protocol) server with OpenAI’s API, enabling your AI applications to access FlowMattic’s powerful automation tools directly through OpenAI’s Responses API. This integration allows you to combine OpenAI’s language models with FlowMattic’s workflow automation capabilities.

Prerequisites

Before you begin, ensure you have:

• OpenAI API account with valid API key
• FlowMattic MCP server configured and running (Server ID: 72)
• FlowMattic authentication token for API access
• Basic knowledge of REST APIs and HTTP requests
• Development environment (for testing API calls)

What You’ll Achieve

After completing this integration, you’ll be able to:

• Use OpenAI models with direct access to FlowMattic tools
• Execute FlowMattic workflows through natural language commands
• Combine AI reasoning with FlowMattic’s automation capabilities
• Build applications that leverage both OpenAI and FlowMattic seamlessly
• Access FlowMattic data and triggers through conversational interfaces

Integration Methods

Method 1: Direct API Call (cURL Example)

This method uses direct HTTP requests to OpenAI’s Responses API with FlowMattic MCP server configuration.

Step 1: Configure the API Request

Use the OpenAI Responses API with your FlowMattic MCP server by making a POST request to the responses endpoint:

curl --location 'https://api.openai.com/v1/responses' \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer $OPENAI_API_KEY' \
--data '{
    "model": "gpt-4.1",
    "tools": [{
        "type": "mcp",
        "server_label": "flowmattic",
        "server_url": "{{flowmattic_mcp_server}}",
        "require_approval": "never",
        "headers": {
            "Authorization": "Bearer YOUR_TOKEN_HERE"
        }
    }],
    "input": "Run the tool to get my recent posts",
    "tool_choice": "required"
}'

Configuration Parameters Explained:

• model: Specifies the OpenAI model to use (gpt-4.1 recommended for complex tasks)
• tools: Array of available tools, including your FlowMattic MCP server
  • type: Always “mcp” for Model Context Protocol servers
  • server_label: Identifier for your FlowMattic server (“flowmattic”)
  • server_url: Your FlowMattic MCP server endpoint
  • require_approval: Set to “never” for automatic tool execution
  • headers: Authentication headers for FlowMattic access
• input: Natural language instruction for the AI
• tool_choice: Set to “required” to ensure tool usage

Step 2: Customize Your Request

Replace placeholders with your actual values:

1. OpenAI API Key: Replace $OPENAI_API_KEY with your actual OpenAI API key
2. FlowMattic Token: Replace YOUR_TOKEN_HERE with your FlowMattic authentication token
3. Server URL: The {{flowmattic_mcp_server}} will be automatically populated with your server URL
4. Input Message: Modify the input to match your specific use case

Example customized request:

curl --location 'https://api.openai.com/v1/responses' \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer sk-your-openai-api-key-here' \
--data '{
    "model": "gpt-4.1",
    "tools": [{
        "type": "mcp",
        "server_label": "flowmattic",
        "server_url": "https://yoursite.com/wp-json/flowmattic/v1/mcp",
        "require_approval": "never",
        "headers": {
            "Authorization": "Bearer your-flowmattic-token"
        }
    }],
    "input": "Create a new workflow to send welcome emails to new subscribers",
    "tool_choice": "required"
}'

Method 2: Python SDK Integration

This method uses the OpenAI Python SDK for easier integration in Python applications.

Step 1: Install the OpenAI Python SDK

First, install the OpenAI Python library:

pip install openai

Step 2: Implement the Integration

Create a Python script to integrate OpenAI with FlowMattic MCP server:

import openai

# Initialize the OpenAI client
client = openai.OpenAI(api_key="your-openai-api-key")

# Make a request with FlowMattic MCP server integration
response = client.responses.create(
    model="gpt-4.1",
    tools=[{
        "type": "mcp",
        "server_label": "flowmattic",
        "server_url": "{{flowmattic_mcp_server}}",
        "require_approval": "never",
        "headers": {
            "Authorization": "Bearer YOUR_TOKEN_HERE"
        }
    }],
    input="Use FlowMattic to get my recent posts",
    tool_choice="required"
)

# Process the response
print(response)

Step 3: Enhanced Python Implementation

For production applications, consider this more robust implementation:

import openai
import os
from typing import Dict, List, Optional

class FlowMatticOpenAIIntegration:
    def __init__(self, openai_api_key: str, flowmattic_token: str, flowmattic_server_url: str):
        """
        Initialize the FlowMattic-OpenAI integration
        
        Args:
            openai_api_key: Your OpenAI API key
            flowmattic_token: Your FlowMattic authentication token
            flowmattic_server_url: Your FlowMattic MCP server URL
        """
        self.client = openai.OpenAI(api_key=openai_api_key)
        self.flowmattic_config = {
            "type": "mcp",
            "server_label": "flowmattic",
            "server_url": flowmattic_server_url,
            "require_approval": "never",
            "headers": {
                "Authorization": f"Bearer {flowmattic_token}"
            }
        }
    
    def execute_flowmattic_task(self, 
                               instruction: str, 
                               model: str = "gpt-4.1",
                               additional_tools: Optional[List[Dict]] = None) -> Dict:
        """
        Execute a task using FlowMattic tools through OpenAI
        
        Args:
            instruction: Natural language instruction for the AI
            model: OpenAI model to use
            additional_tools: Optional additional tools to include
            
        Returns:
            Response from OpenAI API
        """
        tools = [self.flowmattic_config]
        if additional_tools:
            tools.extend(additional_tools)
        
        try:
            response = self.client.responses.create(
                model=model,
                tools=tools,
                input=instruction,
                tool_choice="required"
            )
            return response
        except Exception as e:
            print(f"Error executing FlowMattic task: {e}")
            return {"error": str(e)}

# Usage example
if __name__ == "__main__":
    # Initialize with your credentials
    integration = FlowMatticOpenAIIntegration(
        openai_api_key=os.getenv("OPENAI_API_KEY"),
        flowmattic_token=os.getenv("FLOWMATTIC_TOKEN"),
        flowmattic_server_url=os.getenv("FLOWMATTIC_MCP_SERVER_URL")
    )
    
    # Execute a task
    result = integration.execute_flowmattic_task(
        "Check my recent WordPress posts and create a summary report"
    )
    
    print("Task Result:", result)

Advanced Configuration

Environment Variables Setup

For security and flexibility, use environment variables:

# .env file
OPENAI_API_KEY=sk-your-openai-api-key-here
FLOWMATTIC_TOKEN=your-flowmattic-authentication-token
FLOWMATTIC_MCP_SERVER_URL=https://yoursite.com/wp-json/flowmattic/v1/mcp

Tool Configuration Options

Approval Settings

{
    "require_approval": "never",    // Automatic execution
    "require_approval": "always",   // Manual approval required
    "require_approval": "auto"      // AI decides based on context
}

Custom Headers

{
    "headers": {
        "Authorization": "Bearer YOUR_TOKEN_HERE",
        "X-Custom-Header": "custom-value",
        "User-Agent": "MyApplication/1.0"
    }
}

Multiple Tool Integration

tools = [
    {
        "type": "mcp",
        "server_label": "flowmattic",
        "server_url": "{{flowmattic_mcp_server}}",
        "require_approval": "never",
        "headers": {"Authorization": "Bearer FLOWMATTIC_TOKEN"}
    },
    {
        "type": "mcp",
        "server_label": "other_service",
        "server_url": "https://other-service.com/mcp",
        "require_approval": "auto",
        "headers": {"Authorization": "Bearer OTHER_TOKEN"}
    }
]

Use Cases and Examples

1. Content Management

# Get recent posts and create social media content
instruction = "Get my 5 most recent WordPress posts from FlowMattic and create engaging social media posts for each one"

response = integration.execute_flowmattic_task(instruction)

2. Workflow Automation

# Trigger a FlowMattic workflow based on conditions
instruction = "Check if there are any new contact form submissions in the last hour, and if so, trigger the welcome email workflow"

response = integration.execute_flowmattic_task(instruction)

3. Data Analysis

# Analyze FlowMattic data and generate reports
instruction = "Analyze the performance data from my e-commerce workflows in FlowMattic and create a monthly performance report"

response = integration.execute_flowmattic_task(instruction)

4. Integration Management

# Manage integrations through natural language
instruction = "Create a new FlowMattic workflow that connects my contact form to my email marketing service and Slack notifications"

response = integration.execute_flowmattic_task(instruction)

Error Handling and Troubleshooting

Common Issues and Solutions

Authentication Errors

# Handle authentication issues
try:
    response = client.responses.create(...)
except openai.AuthenticationError as e:
    print(f"OpenAI Authentication failed: {e}")
except Exception as e:
    if "unauthorized" in str(e).lower():
        print("FlowMattic token may be invalid or expired")

Connection Issues

# Handle connection problems
import requests
from requests.exceptions import ConnectionError, Timeout

def test_flowmattic_connection(server_url: str, token: str) -> bool:
    """Test if FlowMattic MCP server is accessible"""
    try:
        headers = {"Authorization": f"Bearer {token}"}
        response = requests.get(f"{server_url}/health", headers=headers, timeout=10)
        return response.status_code == 200
    except (ConnectionError, Timeout):
        return False

Rate Limiting

import time
from openai import RateLimitError

def execute_with_retry(client, **kwargs):
    """Execute request with retry logic for rate limits"""
    max_retries = 3
    for attempt in range(max_retries):
        try:
            return client.responses.create(**kwargs)
        except RateLimitError:
            if attempt == max_retries - 1:
                raise
            wait_time = (2 ** attempt) * 1  # Exponential backoff
            time.sleep(wait_time)

Debugging Tips

1. Enable verbose logging:

   import logging
   logging.basicConfig(level=logging.DEBUG)
   

2. Test components separately:

   • Test OpenAI API access independently
   • Verify FlowMattic MCP server connectivity
   • Validate authentication tokens

3. Monitor API usage:

   • Track OpenAI API usage and costs
   • Monitor FlowMattic server performance
   • Log successful and failed requests

Security Best Practices

API Key Management

• Store API keys in environment variables or secure vaults
• Never commit API keys to version control
• Rotate keys regularly
• Use different keys for development and production

Token Security

• Secure FlowMattic authentication tokens
• Implement token refresh mechanisms
• Monitor for unauthorized API usage
• Use HTTPS for all communications

Access Control

# Implement access control for sensitive operations
ALLOWED_OPERATIONS = [
    "get_posts",
    "create_workflow",
    "send_notification"
]

def validate_operation(instruction: str) -> bool:
    """Validate if the requested operation is allowed"""
    # Implement your validation logic
    return any(op in instruction.lower() for op in ALLOWED_OPERATIONS)

Performance Optimization

Caching Strategies

import functools
import time

@functools.lru_cache(maxsize=100)
def cached_flowmattic_request(instruction_hash: str):
    """Cache FlowMattic responses for repeated requests"""
    # Implementation depends on your caching strategy
    pass

Batch Operations

def batch_flowmattic_operations(instructions: List[str]) -> List[Dict]:
    """Process multiple FlowMattic operations efficiently"""
    results = []
    for instruction in instructions:
        result = integration.execute_flowmattic_task(instruction)
        results.append(result)
        time.sleep(0.1)  # Rate limiting
    return results

Async Processing

import asyncio
import aiohttp

async def async_flowmattic_request(instruction: str):
    """Asynchronous FlowMattic request processing"""
    # Implement async version for high-throughput applications
    pass

Monitoring and Analytics

Request Tracking

import uuid
from datetime import datetime

class RequestTracker:
    def __init__(self):
        self.requests = []
    
    def log_request(self, instruction: str, response: Dict):
        """Log request for analytics"""
        self.requests.append({
            "id": str(uuid.uuid4()),
            "timestamp": datetime.now(),
            "instruction": instruction,
            "success": "error" not in response,
            "response_size": len(str(response))
        })

Performance Metrics

def calculate_metrics(requests: List[Dict]) -> Dict:
    """Calculate performance metrics"""
    total_requests = len(requests)
    successful_requests = sum(1 for r in requests if r["success"])
    success_rate = successful_requests / total_requests if total_requests > 0 else 0
    
    return {
        "total_requests": total_requests,
        "success_rate": success_rate,
        "average_response_time": calculate_avg_response_time(requests)
    }

Additional Resources

• OpenAI API Documentation - Complete OpenAI API reference
• FlowMattic MCP Documentation - FlowMattic MCP integration guide
• MCP Protocol Specification - Technical details about Model Context Protocol
• OpenAI Python SDK - Official Python library documentation

Next Steps

After successfully integrating OpenAI API with FlowMattic MCP server:

1. Explore advanced AI applications - Build conversational interfaces for workflow management
2. Implement custom tools - Create specialized FlowMattic tools for your specific use cases
3. Scale your integration - Deploy to production with proper monitoring and error handling
4. Build user interfaces - Create web or mobile apps that leverage this integration
5. Share your experience - Contribute to the community with your implementations and use cases

Your OpenAI-powered applications can now seamlessly interact with FlowMattic’s automation capabilities, opening up endless possibilities for AI-driven workflow management and automation.