# 🤖 Dialogflow CX MCP Server 🚀 ![Dialogflow CX](https://img.shields.io/badge/Dialogflow_CX-4285F4?style=for-the-badge&logo=google-cloud&logoColor=white) ![MCP](https://img.shields.io/badge/MCP-Server-00C7B7?style=for-the-badge&logo=serverfault&logoColor=white) ![Python](https://img.shields.io/badge/Python-3.12-3776AB?style=for-the-badge&logo=python&logoColor=white) 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 ```bash # 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 ```bash # 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: ```bash export DIALOGFLOW_API_KEY=your_api_key export PROJECT_ID=your_project_id export LOCATION=your_location export AGENT_ID=your_agent_id ``` ## 📊 Architecture ```mermaid 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. ```python 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. ```python 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. ```python 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. ```python 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: ```python # 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

```json { "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](https://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 [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](LICENSE) ## 👥 Contributing Contributions are welcome! Please feel free to submit a Pull Request. ### Contribution Workflow 1. 🍴 Fork the repository 2. 🔧 Create a feature branch (`git checkout -b feature/amazing-feature`) 3. 💻 Commit your changes (`git commit -m 'Add some amazing feature'`) 4. 🚀 Push to the branch (`git push origin feature/amazing-feature`) 5. 🔍 Open a Pull Request ---

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