🤖 Dialogflow CX MCP Server 🚀

Name: 🤖 Dialogflow CX MCP Server 🚀
Author: Yash-Kavaiya

@Yash-Kavaiya

リポジトリ

🤖 Dialogflow CX MCP Server 🚀 について

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

基本情報

カテゴリ

AI とエージェント

ランタイム

python

トランスポート

stdio

公開者

Yash-Kavaiya

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",
        "."
      ]
    }
  }
}

ツール

ツールは検出されませんでした

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

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.

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