MCP Server Conversation Agents

An MCP server implementation designed for Google Dialogflow CX, providing seamless integration between AI assistants and Dialogflow CX.

Artificial intelligence chatbots Customer support #Conversation Integration #Intent Recognition #Session Management #Voice Processing .Python

rating : 2.5 points

downloads : 7.7K

update time : 2025-04-28

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What is Dialogflow CX MCP Server?

This server acts as a communication bridge that allows AI assistants to connect with Google's Dialogflow CX platform. It handles all the complex interactions like understanding user requests, managing conversations, and processing responses from Dialogflow's powerful language understanding system.

How to use Dialogflow CX MCP Server?

You can set up the server either through Docker for quick deployment or install it manually. After configuration with your Google Cloud credentials, your AI assistant can start sending requests to Dialogflow CX through the server's API endpoints.

Use Cases

Ideal for businesses that want to enhance their AI assistants with Google's advanced conversation capabilities. Common scenarios include customer support chatbots, voice assistants, and automated appointment scheduling systems.

Key Features

Two-Way Communication

Enables seamless back-and-forth conversations between your AI assistant and Dialogflow CX

Smart Intent Recognition

Accurately identifies what users want from their messages or voice inputs

Voice Command Support

Processes audio inputs and converts them to understandable requests

Conversation Memory

Remembers context across multiple interactions for natural conversations

Advantages

Leverages Google's powerful natural language understanding

Handles complex conversation flows automatically

Supports both text and voice interactions

Easy integration with existing AI assistants

Limitations

Requires Google Cloud project setup

Needs configuration for optimal performance

Audio processing requires specific formats

Getting Started

Set Up Your Environment

Install Docker or Python 3.12+ on your system

Get Google Cloud Credentials

Create a service account in Google Cloud Console with Dialogflow CX access

Configure the Server

Set your project details as environment variables or in a config file

Start the Server

Run the server using Docker or directly through Python

Example Scenarios

Customer Support Chatbot

Handle common customer questions about products and services

Appointment Scheduling

Let users book appointments through natural conversation

Frequently Asked Questions

Do I need programming skills to use this?

Can I use this with my existing chatbot?

What audio formats are supported?

Helpful Resources

Dialogflow CX Documentation

Official Google documentation for Dialogflow CX

Example Implementation

Sample code repository for the MCP server

Google Cloud Setup Guide

Instructions for setting up Google Cloud services

🚀 🤖 Dialogflow CX MCP Server 💡

A MCP server for managing Dialogflow CX sessions, supporting multiple interaction modes and webhook integration.

✨ Features

🌟 Core Features

Intent Matching: Identify user intents through text or audio input.
Context Management: Maintain the conversation state and handle parameter updates.
Multi-turn Dialogues: Support complex interaction flows and maintain session coherence.
Webhook Integration: Seamlessly connect with third-party services.

🎯 Extended Features

Custom Rules: Flexibly configure intent routing and parameter processing logic.
Log Tracking: Record every operation step for easy debugging and analysis.
State Recovery: Reconnect after disconnection to ensure uninterrupted sessions.

📦 Installation

🌐 Environment Requirements

Operating System: Linux/MacOS/Windows (Linux is recommended).
Python Version: 3.8 or higher.
Dependency Management Tool: pip

💽 Project Installation

git clone https://github.com/your-repository.git
cd mcp-server
pip install -r requirements.txt

📚 Documentation

🛠️ Configuration

Add the following content to the config.yaml file:

server:
  host: '0.0.0.0'
  port: 5005
  
dialogflow:
  project_id: 'your-project-id'
  private_key_path: './path/to/service-account.json'
  
webhook:
  enable: true
  url: 'https://api.your-domain.com/webhook'

🚀 Quick Start

🏷️ Starting the Service

python3 server.py

🔗 Example Interactions

Text Input

curl -X POST http://localhost:5005/api/conversations \
 -H "Content-Type: application/json" \
 -d '{"text": "你好", "session_id": "user_123"}'

Audio Processing

curl -X POST http://localhost:5005/api/audio \
 -H "Content-Type: audio/wav" \
 -d '@/path/to/audio.wav'

💻 Usage Examples

💡 Example Response

{
  "messages": [
    {
      "type": "text",
      "content": "Welcome! How can I help you?"
    }
  ],
  "intent": {
    "name": "greeting",
    "confidence": 0.95
  },
  "parameters": {
    "user_name": "John"
  },
  "current_page": "WelcomePage",
  "session_id": "user_123",
  "end_interaction": false
}