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🤖 Dialogflow CX MCP Server 🚀

@Yash-Kavaiya

Repository

About 🤖 Dialogflow CX MCP Server 🚀

🤖 MCP (Model Context Protocol) server implementation for conversation agents with multi-agent orchestration

Config

Add this server to your MCP-compatible client using the configuration below.

{
  "mcpServers": {
    "mcp-server-conversation-agents": {
      "command": "docker",
      "args": [
        "build",
        "-t",
        "dialogflow-cx-mcp",
        "."
      ]
    }
  }
}

Tools

No tools detected

We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.

Overview

What is Dialogflow CX MCP Server?

A Model Control Protocol (MCP) server implementation for Google Dialogflow CX, enabling seamless integration between AI assistants and Google's conversational platform. It handles managing conversations, processing intent detection, and interfacing with Google's NLU systems.

How to use Dialogflow CX MCP Server?

Install via Docker or manual Python (Python 3.12+ required). Configure environment variables: dialogflowApiKey, projectId, location, agentId. The server exposes tools like initialize_dialogflow, detect_intent, detect_intent_from_audio, match_intent, and webhook handling.

Key features of Dialogflow CX MCP Server

  • 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

Use cases of Dialogflow CX MCP Server

  • Integrating AI assistants with Dialogflow CX for conversational agents
  • Detecting intents from text or audio input
  • Managing multi-turn conversations with session persistence
  • Handling webhooks for external service integration
  • Matching intents without affecting conversation state

FAQ from Dialogflow CX MCP Server

What is the Model Control Protocol?

It is a protocol that allows AI assistants to interact with Dialogflow CX agents through a standardized interface.

What are the runtime requirements?

Python 3.12 or higher, a Google Cloud project with Dialogflow CX enabled, API credentials, and a configured Dialogflow CX agent.

How do I configure the server?

Set environment variables dialogflowApiKey, projectId, location, and agentId. Optionally provide a credentials path for initialization.

What tools does the server expose?

initialize_dialogflow, detect_intent, detect_intent_from_audio, match_intent, parse_webhook_request, and create_webhook_response.

Does it support audio input?

Yes, via the detect_intent_from_audio tool; supports audio file path and encoding parameters.

Frequently asked questions

What is the Model Control Protocol?

It is a protocol that allows AI assistants to interact with Dialogflow CX agents through a standardized interface.

What are the runtime requirements?

Python 3.12 or higher, a Google Cloud project with Dialogflow CX enabled, API credentials, and a configured Dialogflow CX agent.

How do I configure the server?

Set environment variables `dialogflowApiKey`, `projectId`, `location`, and `agentId`. Optionally provide a credentials path for initialization.

What tools does the server expose?

`initialize_dialogflow`, `detect_intent`, `detect_intent_from_audio`, `match_intent`, `parse_webhook_request`, and `create_webhook_response`.

Does it support audio input?

Yes, via the `detect_intent_from_audio` tool; supports audio file path and encoding parameters.

Comments

Basic information

Category

AI & Agents

Transports

stdio

Author

Yash-Kavaiya

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