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Dialogflow CX MCP Server

@Yash-Kavaiya

リポジトリ

基本情報

カテゴリ

AI とエージェント

トランスポート

stdio

作者

Yash-Kavaiya

Dialogflow CX MCP Server について

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

Dialogflow CX MCP Server の設定

以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。

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

Dialogflow CX MCP Server のツール

ツール一覧はまだありません

この掲載には表示できるツール一覧がまだありません。サーバーにツールがないという意味ではありません。対応機能はプロジェクトのドキュメントをご確認ください。

ツールは README から自動的に抽出されます。メンテナーは ## Tools という見出しの下に記載することで、このタブに反映できます。

概要

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

よくある質問

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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