🤖 Dialogflow CX MCP Server 🚀

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

@Yash-Kavaiya

Repository

About 🤖 Dialogflow CX MCP Server 🚀

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

Basic information

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.

Comments

More AI & Agents MCP servers

MCP Manager for Claude Desktop

zueai

simple web ui to manage mcp (model context protocol) servers in the claude app

290

🛡️ A.I.G（AI-Infra-Guard）

Tencent

A full-stack AI Red Teaming platform securing AI ecosystems via OpenClaw Security Scan, Agent Scan, Skills Scan, MCP scan, AI Infra scan and LLM jailbreak evaluation.

4.1K

MCP Claude Code

SDGLBL

MCP implementation of Claude Code capabilities and more

302

meGPT - upload an author's content into an LLM

adrianco

Code to process many kinds of content by an author into an MCP server

287

MCP Client for Ollama (ollmcp)

jonigl

Harness the power of local LLMs with this TUI MCP Client for Ollama. Featuring all core MCP primitives (tools, prompts, resources), agent mode, multi-server, model switching, streaming responses, human-in-the-loop, thinking mode, model params config, system prompts, and saved pre

768