How to build mcp server from scratch: Beginner’s Guide🥳🔥

#mcp #webdev #ai #showdev

Think about how much time you spend scheduling meetings, checking the weather, sending emails, or jotting down notes throughout the day. These small tasks, though simple, add up and that’s where AI voice agents can help.

Voice assistants powered by VideoSDK, combined with MCP (Model Context Protocol), allow you to integrate your assistant with real-world tools Google Calendar, Notion, weather APIs, reminder apps, and more. With just a voice command, you can automate tasks, fetch data, or control devices seamlessly.

For developers, this is a golden opportunity to create customized workflows and integrations without reinventing the wheel. For users, it means smarter interactions and less manual effort.

📖 What is MCP Integration?

MCP (Model Context Protocol) is a flexible communication layer that allows your AI voice agent to exchange data and events with external services. Whether you're using a local MCP server hosted on your machine or a cloud-based automation platform like Zapier, MCP enables your agent to trigger actions such as scheduling meetings, setting reminders, or fetching information all through voice commands.

Key Components:

Agent :The AI voice interface built with VideoSDK that listens to user input and dispatches requests.
MCP Server : Acts as a bridge, connecting the agent to external tools and APIs.
Integrations : Services like Google Calendar, Notion, or weather APIs that the MCP server can trigger.

📂 Local vs Cloud MCP Integration

Feature	Local MCP Server	Cloud Integration (Zapier, etc.)
Hosting	Your machine or private server	Third-party automation platforms
Security	You control everything	Easier to implement but depends on cloud provider
Flexibility	Customizable workflows	Pre-built connectors and triggers
Use Case	Internal apps, data privacy	Fast deployment, multi-step automation

💥 How to integrate VideoSdk AI Agents with Mcp server

MCP follows a client-server architecture, where the AI agent communicates with external services using structured requests and responses. With VideoSDK’s built-in MCP support, you can easily connect your agent to external Python scripts or APIs for advanced workflows.

⚙ Installation Prerequisites

pip install fastmcp  # For creating MCP servers
pip install videosdk-agents  # VideoSDK agents with MCP support

1. STDIO Transport (MCPServerStdio) : For local process MCPServerStdio

import sys
from pathlib import Path
from videosdk.agents import Agent, MCPServerStdio

# Path to the external Python script (recipe service)
mcp_server_path = Path(__file__).parent / "recipe_service.py"

class RecipeAgent(Agent):
    def __init__(self):
        super().__init__(
            instructions="You are a cooking assistant. Use external services to suggest recipes.",
            mcp_servers=[
                MCPServerStdio(
                    executable_path=sys.executable,
                    process_arguments=[str(mcp_server_path)],
                    session_timeout=30
                )
            ]
        )

# Usage:
# User says: "Suggest a recipe with chicken and rice"
# Agent calls the external MCP server script
# Script returns: "Try chicken fried rice with vegetables"

2. HTTP Transport ( MCPServerHTTP) : For remote service communication:

from videosdk.agents import MCPServerHTTP

mcp_servers=[
    MCPServerHTTP(
        endpoint_url="https://your-mcp-server.com/api/mcp",
        session_timeout=30
    )
]

Multiple MCP Servers

mcp_servers=[
    MCPServerStdio(
        executable_path=sys.executable,
        process_arguments=[str(mcp_script)],
        session_timeout=30
    ),
    MCPServerHTTP(
        endpoint_url="https://mcp.zapier.com/api/mcp/s/your-server-id",
        session_timeout=30
    )
]

Github code full implementation : https://github.com/videosdk-live/agents-quickstart/blob/main/MCP/mcp_agent.py

Conclusion

With VideoSDK AI voice agents and MCP servers, developers can automate workflows like meetings, reminders, Notion updates, weather updates, and emails through voice commands. Both local scripts and cloud integrations are supported, making automation flexible and scalable.

We hope this deep dive into building AI voice agents with VideoSDK and MCP servers has been helpful.

💡 We’d love to hear from you!

Did you try implementing any of the examples above?
Are there specific day-to-day workflows you want your AI agent to handle?
Do you have questions about setting up MCP servers locally?

Drop your thoughts, questions, or experiences in the comments below (or in discord), and let’s make AI voice agents even more practical and powerful together!