📖 Blog Post: Learn more about this project in the detailed blog post: Telephony MCP Server for Agentic AI and Language Models

Telephony MCP Server

Demo Using Claude Desktop

Agentic Telephony Conversation with Speech Recognition

Use SMS during mid-conversation

SMS Enquiry (Send and Receive)

Demo Using GitHub Copilot

Introduction

This directory contains MCP (Model Context Protocol) Server tools for telephony operations, such as making voice calls and sending SMS messages using the Vonage API. These tools are designed to be integrated with Large Language Model (LLM) applications, enabling LLMs to perform real-world actions beyond simple text generation.

LLMs and Tool Integration

LLMs (Large Language Models) are advanced token generators—they can generate text, images, or even video based on input prompts. However, their core capability is limited to generating content; they cannot access external data or perform actions in the real world on their own.

To extend their functionality, LLMs can be connected to external tools. For example, when a user asks, "What's the weather today?" the LLM can invoke a backend API tool like get_weather(city) via a system prompt, parse the response, and return the result to the user. This tool-calling mechanism transforms a basic LLM into a powerful LLM Application.

Tool Calling with MCP and LangChain

• LangChain is a popular framework for developing applications powered by LLMs. It provides a collection of pre-built tools (called a Toolkit) that LLMs can use to interact with external systems.
• MCP (Model Context Protocol) follows the same concept: it offers a collection of pre-built tools and a framework for writing new tools and handling function calling.
• Both frameworks allow LLMs to invoke tools, parse their outputs, and integrate the results into their responses.

How This Works

1. Tool Definition: In this project, tools like voice_call and send_sms are defined using the MCP framework. Each tool is a function that can be called by an LLM application.
2. LLM Application: When integrated with an LLM (such as OpenAI's GPT, Anthropic's Claude, etc.), the LLM can decide to call these tools based on user prompts.
3. Execution Flow:
   • The LLM receives a prompt (e.g., "Call Alice and say hello").
   • The LLM determines that a tool invocation is needed and calls the appropriate MCP tool (e.g., voice_call).
   • The tool executes (e.g., initiates a phone call via Vonage) and returns the result.
   • The LLM parses the response and presents it to the user.

Running the MCP Tools

Prerequisites

• Python 3.13+
• MCP CLI (mcp[cli]), FastAPI, httpx, pyjwt, python-dotenv, uvicorn, pydantic (see pyproject.toml for details)
• Vonage API credentials (API key, secret, application ID, private key)
• Public URL for callback server (for production use)

... View full README on GitHub