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语音通话 MCP 服务器

Organization
popcornspace

支持 Claude 及其他 AI 助手通过 Twilio 和 OpenAI(GPT-4o 实时模型)发起和管理语音通话的模型上下文协议(MCP)服务器。需要设置环境变量以配置 Twilio 凭据、OpenAI API 密钥和 ngrok 认证令牌。

Publisherpopcornspace
Repositoryvoice-call-mcp-server
LanguageTypeScript
Forks
18
Stars
59
Available tools
0
Transport typestdio
Categories
LicenseMIT
Links
  • Connect tools to AI workflows

    语音通话 MCP 服务器 exposes MCP capabilities that can be used by compatible AI clients and agents.

  • 0 available tools

    Browse the callable actions below, including names and descriptions when provided by the server.

  • Ready-to-copy setup

    Use the installation snippets to configure this server in your preferred MCP client.

  • Open source signals

    59 stars and 18 forks from the linked repository.

Voice Call MCP Server

A Model Context Protocol (MCP) server that enables Claude and other AI assistants to initiate and manage voice calls using Twilio and OpenAI (GPT-4o Realtime model).

Use this as a base to kick-start your AI-powered voice calling explorations, save time and develop additional functionality on top of it.

Demo

Sequence Diagram

mermaid
sequenceDiagram
    participant AI as AI Assistant (e.g., Claude)
    participant MCP as MCP Server
    participant Twilio as Twilio
    participant Phone as Destination Phone
    participant OpenAI as OpenAI
    
    AI->>MCP: 1) Initiate outbound call request <br>(POST /calls)
    MCP->>Twilio: 2) Place outbound call via Twilio API
    Twilio->>Phone: 3) Ring the destination phone
    Twilio->>MCP: 4) Call status updates & audio callbacks (webhooks)
    MCP->>OpenAI: 5) Forward real-time audio to OpenaAI's realtime model
    OpenAI->>MCP: 6) Return voice stream
    MCP->>Twilio: 7) Send voice stream
    Twilio->>Phone: 8) Forward voice stream
    Note over Phone: Two-way conversation continues <br>until the call ends

Features

  • Make outbound phone calls via Twilio 📞
  • Process call audio in real-time with GPT-4o Realtime model 🎙️
  • Real-time language switching during calls 🌐
  • Pre-built prompts for common calling scenarios (like restaurant reservations) 🍽️
  • Automatic public URL tunneling with ngrok 🔄
  • Secure handling of credentials 🔒

Why MCP?

The Model Context Protocol (MCP) bridges the gap between AI assistants and real-world actions. By implementing MCP, this server allows AI models like Claude to:

  1. Initiate actual phone calls on behalf of users
  2. Process and respond to real-time audio conversations
  3. Execute complex tasks requiring voice communication

This open-source implementation provides transparency and customizability, allowing developers to extend functionality while maintaining control over their data and privacy.

Requirements

  • Node.js >= 22
    • If you need to update Node.js, we recommend using nvm (Node Version Manager):
      bash
      nvm install 22
      nvm use 22
  • Twilio account with API credentials
  • OpenAI API key
  • Ngrok Authtoken

Installation

Manual Installation

  1. Clone the repository

    bash
    git clone https://github.com/lukaskai/voice-call-mcp-server.git
    cd voice-call-mcp-server
  2. Install dependencies and build

    bash
    npm install
    npm run build

Configuration

The server requires several environment variables:

  • TWILIO_ACCOUNT_SID: Your Twilio account SID
  • TWILIO_AUTH_TOKEN: Your Twilio auth token
  • TWILIO_NUMBER: Your Twilio number
  • OPENAI_API_KEY: Your OpenAI API key
  • NGROK_AUTHTOKEN: Your ngrok authtoken
  • RECORD_CALLS: Set to "true" to record calls (optional)

Claude Desktop Configuration

To use this server with Claude Desktop, add the following to your configuration file:

macOS: ~/Library/Application Support/Claude/claude_desktop_config.json

Windows: %APPDATA%\Claude\claude_desktop_config.json

json
{
  "mcpServers": {
    "voice-call": {
      "command": "node",
      "args": ["/path/to/your/mcp-new/dist/start-all.cjs"],
      "env": {
        "TWILIO_ACCOUNT_SID": "your_account_sid",
        "TWILIO_AUTH_TOKEN": "your_auth_token",
        "TWILIO_NUMBER": "your_e.164_format_number",
        "OPENAI_API_KEY": "your_openai_api_key",
        "NGROK_AUTHTOKEN": "your_ngrok_authtoken"
      }
    }
  }
}

After that, restart Claude Desktop to reload the configuration. If connected, you should see Voice Call under the 🔨 menu.

Example Interactions with Claude

Here are some natural ways to interact with the server through Claude:

  1. Simple call:
Can you call +1-123-456-7890 and let them know I'll be 15 minutes late for our meeting?
  1. Restaurant reservation:
Please call Delicious Restaurant at +1-123-456-7890 and make a reservation for 4 people tonight at 7:30 PM. Please speak in German.
  1. Appointment scheduling:
Please call Expert Dental NYC (+1-123-456-7899) and reschedule my Monday appointment to next Friday between 4–6pm.

Important Notes

  1. Phone Number Format: All phone numbers must be in E.164 format (e.g., +11234567890)
  2. Rate Limits: Be aware of your Twilio and OpenAI account's rate limits and pricing
  3. Voice Conversations: The AI will handle natural conversations in real-time
  4. Call Duration: Be mindful of call durations as they affect OpenAI API and Twilio costs
  5. Public Exposure: Be aware that the ngrok tunnel exposes your server publicly for Twilio to reach it (though with a random URL and protected by a random secret)

Troubleshooting

Common error messages and solutions:

  1. "Phone number must be in E.164 format"

    • Make sure the phone number starts with "+" and the country code
  2. "Invalid credentials"

    • Double-check your TWILIO_ACCOUNT_SID and TWILIO_AUTH_TOKEN. You can copy them from the Twilio Console
  3. "OpenAI API error"

    • Verify your OPENAI_API_KEY is correct and has sufficient credits
  4. "Ngrok tunnel failed to start"

    • Ensure your NGROK_AUTHTOKEN is valid and not expired
  5. "OpenAI Realtime does not detect the end of voice input, or is lagging."

    • Sometimes, there might be voice encoding issues between Twilio and the receiver's network operator. Try using a different receiver.

Contributing

Contributions are welcome! Here are some areas we're looking to improve:

  • Implement support for multiple AI models beyond the current implementation
  • Add database integration to store conversation history locally and make it accessible for AI context
  • Improve latency and response times to enhance call experiences
  • Enhance error handling and recovery mechanisms
  • Add more pre-built conversation templates for common scenarios
  • Implement improved call monitoring and analytics

If you'd like to contribute, please open an issue to discuss your ideas before submitting a pull request.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Security

Please do not include any sensitive information (like phone numbers or API credentials) in GitHub issues or pull requests. This server handles sensitive communications; deploy it responsibly and ensure all credentials are kept secure.

Time For a New Mission?

We’re hiring engineers to build at the frontier of voice AI — and bake it into a next-gen telco.

Curious? Head to careers.popcorn.space 🍿 !

Installation

TypingMind
Prerequisites:

Node.js 18+

{
  "mcpServers": {
    "lukaskai-voice-call-mcp-server": {
      "command": "",
      "args": []
    }
  }
}

Use 语音通话 MCP 服务器 MCP with multiple AI models

TypingMind connects MCP tools at the workspace level, so once 语音通话 MCP 服务器 is connected, you can use it with different AI models in TypingMind instead of setting it up separately for each model. This MCP runs locally through the TypingMind MCP connector on your device.

Setup guide to use the local connector

Use this when the MCP server needs access to local files, apps, or private resources on your computer.

1

Open the MCP settings

In TypingMind, go to Settings, Advanced Settings, then Model Context Protocol and choose Setup Connector.

  1. Open TypingMind in your browser.
  2. Click the Settings icon.
  3. Go to Advanced Settings.
  4. Open the Model Context Protocol section.
  5. Click Setup Connector and choose This Device.
TypingMind MCP connector setup screen with This Device selected
2

Run the connector command

Choose This Device, copy the command from TypingMind, and run it in Terminal. Keep the process running while you use MCP.

  1. Copy the setup command shown by TypingMind.
  2. Open Terminal on macOS or Windows Terminal on Windows.
  3. Paste and run the command.
  4. Approve the package install if Terminal asks you to proceed.
  5. Keep the Terminal window running while using MCP tools.
3

Add 语音通话 MCP 服务器 as a server

When the connector status is Ready, click Edit Servers and paste the MCP server configuration.

  1. Wait until the connector status shows Ready.
  2. Click Edit Servers.
  3. Paste the 语音通话 MCP 服务器 MCP server configuration.
  4. Save the server list.
  5. Refresh if you want to confirm the connector is still ready.
TypingMind MCP settings showing active server and Edit Servers button
{
  "mcpServers": {
    "lukaskai-voice-call-mcp-server": {
      "command": "npx",
      "args": [
        "-y",
        "voice-call-mcp-server"
      ]
    }
  }
}
4

Use it across models

Save the server list, open Plugins, enable the 语音通话 MCP 服务器 MCP tools, then select any supported AI model in TypingMind and use the tools in chat or assign them to an AI agent.

  1. Open the Plugins page in TypingMind.
  2. Enable the 语音通话 MCP 服务器 MCP tools.
  3. Start a chat and choose the AI model you want to use.
  4. Use the MCP tools in chat or assign them to an AI agent.
  5. Switch to another AI model whenever needed without reconnecting MCP.
TypingMind chat using enabled MCP tools with a selected AI model
Can you use 语音通话 MCP 服务器 to help me with this task?
语音通话 MCP 服务器
Sure. I read it.
Here is what I found using 语音通话 MCP 服务器.

Frequently asked questions

What is the 语音通话 MCP 服务器 MCP server used for?

语音通话 MCP 服务器 is an MCP server that lets compatible AI clients connect to external tools and context. In TypingMind, you can add this MCP server once and make its tools available in your AI workspace.

Can I use 语音通话 MCP 服务器 MCP with multiple AI models in TypingMind?

Yes. TypingMind connects MCP tools at the workspace level, so you can use 语音通话 MCP 服务器 with different AI models such as Claude, ChatGPT, Gemini, or other models you have configured in TypingMind without setting up the MCP server separately for each model.

Why use 语音通话 MCP 服务器 MCP with TypingMind?

TypingMind is one of the best frontends for LLM chat because it brings multiple AI models, prompts, plugins, AI agents, API keys, and MCP tools into one workspace. With 语音通话 MCP 服务器 connected, you can use its MCP tools across your preferred models while keeping your chat workflow organized in TypingMind.

How do I connect 语音通话 MCP 服务器 MCP to TypingMind?

语音通话 MCP 服务器 runs through the TypingMind local MCP connector. This is best when the MCP server needs access to local files, desktop apps, command-line tools, or private resources on your computer.

What tools does 语音通话 MCP 服务器 MCP provide in TypingMind?

语音通话 MCP 服务器 exposes MCP capabilities that can be enabled from the TypingMind Plugins page and used in chat or assigned to AI agents.

Do I need to share my API keys with TypingMind to use 语音通话 MCP 服务器 MCP?

No. TypingMind is local-first and lets you keep your model providers, API keys, prompts, and MCP configuration under your control. If 语音通话 MCP 服务器 requires authentication, add the required headers, OAuth settings, or local configuration for that MCP server when you create the connection.

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