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Dialogflow CX MCP Server

@Yash-Kavaiya

代码仓库

基本信息

分类

AI 与智能体

传输方式

stdio

作者

Yash-Kavaiya

关于 Dialogflow CX MCP Server

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

Dialogflow CX MCP Server 配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

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

Dialogflow CX MCP Server 工具

工具列表暂未提供

当前收录暂时没有可展示的工具列表，不代表服务没有工具。请查阅项目文档了解支持的能力。

工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。

概览

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

常见问题

1

What is the Model Control Protocol?

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

2

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.

3

How do I configure the server?

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

4

What tools does the server expose?

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

5

Does it support audio input?

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

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