mcp-server-conversation-agents
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A powerful Model Control Protocol (MCP) server implementation for Google Dialogflow CX, enabling seamless integration between AI assistants and Google's advanced conversational platform.
🤖 Dialogflow CX MCP Server 🚀
A powerful Model Control Protocol (MCP) server implementation for Google Dialogflow CX, enabling seamless integration between AI assistants and Google's advanced conversational platform.
💡 Pro Tip: This server bridges the gap between AI assistants and Dialogflow CX, unlocking powerful conversational capabilities!
📋 Overview
This project provides a suite of tools that allow AI assistants to interact with Dialogflow CX agents through a standardized protocol. The server handles all the complexity of managing conversations, processing intent detection, and interfacing with Google's powerful NLU systems.
✨ Key Features
- 🔄 Bidirectional communication with Dialogflow CX
- 🎯 Intent detection and matching capabilities
- 🎤 Audio processing for speech recognition
- 🔌 Webhook request/response handling
- 📝 Session management for persistent conversations
- 🔒 Secure API authentication
🔧 Requirements
| Requirement | Description | Version |
|---|---|---|
| 🐍 Python | Programming language | 3.12+ |
| ☁️ Google Cloud | Project with Dialogflow CX enabled | Latest |
| 🤖 Dialogflow CX | Conversational agent | Latest |
| 🔑 API Credentials | Authentication for Google services | - |
🚀 Installation
🐳 Using Docker
# Clone the repository
git clone https://github.com/Yash-Kavaiya/mcp-server-conversation-agents.git
cd mcp-server-conversation-agents
# Build the Docker image
docker build -t dialogflow-cx-mcp .
# Run the container
docker run -it dialogflow-cx-mcp
💻 Manual Installation
# Clone the repository
git clone https://github.com/Yash-Kavaiya/mcp-server-conversation-agents.git
cd mcp-server-conversation-agents
# Create a virtual environment (optional but recommended)
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
# Install the package
pip install -e .
⚙️ Configuration
You'll need to provide the following configuration parameters:
| Parameter | Description | Example |
|---|---|---|
dialogflowApiKey | Your Dialogflow API key | "abc123def456" |
projectId | Google Cloud project ID | "my-dialogflow-project" |
location | Location of the agent | "us-central1" |
agentId | ID of your Dialogflow CX agent | "12345-abcde-67890" |
These can be set as environment variables:
export DIALOGFLOW_API_KEY=your_api_key
export PROJECT_ID=your_project_id
export LOCATION=your_location
export AGENT_ID=your_agent_id
📊 Architecture
graph TD
A[AI Assistant] <-->|MCP Protocol| B[MCP Server]
B <-->|Google API| C[Dialogflow CX]
C <-->|NLU Processing| D[Intent Detection]
C <-->|Conversation Management| E[Session Management]
B <-->|Webhooks| F[External Services]
🛠️ Usage
The MCP server exposes the following tools for AI assistants:
🔍 initialize_dialogflow
Initialize the Dialogflow CX client with your project details.
await initialize_dialogflow(
project_id="your-project-id",
location="us-central1",
agent_id="your-agent-id",
credentials_path="/path/to/credentials.json" # Optional
)
💬 detect_intent
Detect intent from text input.
response = await detect_intent(
text="Hello, how can you help me?",
session_id="user123", # Optional
language_code="en-US" # Optional
)
🎤 detect_intent_from_audio
Process audio files to detect intent.
response = await detect_intent_from_audio(
audio_file_path="/path/to/audio.wav",
session_id="user123", # Optional
sample_rate_hertz=16000, # Optional
audio_encoding="AUDIO_ENCODING_LINEAR_16", # Optional
language_code="en-US" # Optional
)
🎯 match_intent
Match intent without affecting the conversation session.
response = await match_intent(
text="What are your hours?",
session_id="user123", # Optional
language_code="en-US" # Optional
)
🔄 Webhook Handling
Parse webhook requests and create webhook responses:
# Parse a webhook request
parsed_request = await parse_webhook_request(request_json)
# Create a webhook response
response = await create_webhook_response({
"messages": ["Hello! How can I help you today?"],
"parameter_updates": {"user_name": "John"}
})
🔧 Response Format
Here's an example of the response format:
📋 Click to expand
{
"messages": [
{
"type": "text",
"content": "Hello! How can I help you today?"
}
],
"intent": {
"name": "greeting",
"confidence": 0.95
},
"parameters": {
"user_name": "John"
},
"current_page": "Welcome Page",
"session_id": "user123",
"end_interaction": false
}
🔗 Smithery Integration
This project is configured to work with Smithery.ai, a platform that allows for easy deployment and management of MCP servers.
💡 Pro Tip: Smithery.ai integration enables one-click deployment and simplified management of your Dialogflow CX MCP server!
📄 License
👥 Contributing
Contributions are welcome! Please feel free to submit a Pull Request.
Contribution Workflow
- 🍴 Fork the repository
- 🔧 Create a feature branch (
git checkout -b feature/amazing-feature) - 💻 Commit your changes (
git commit -m 'Add some amazing feature') - 🚀 Push to the branch (
git push origin feature/amazing-feature) - 🔍 Open a Pull Request
Built with ❤️ by the MCP Server team
