Skip to main content

Add your description here

Project description

MCP Server Example

This repository contains an implementation of a Model Context Protocol (MCP) server for educational purposes. This code demonstrates how to build a functional MCP server that can integrate with various LLM clients.

To follow the complete tutorial, please refer to the YouTube video tutorial.

What is MCP?

MCP (Model Context Protocol) is an open protocol that standardizes how applications provide context to LLMs. Think of MCP like a USB-C port for AI applications - it provides a standardized way to connect AI models to different data sources and tools.

MCP Diagram

Key Benefits

  • A growing list of pre-built integrations that your LLM can directly plug into
  • Flexibility to switch between LLM providers and vendors
  • Best practices for securing your data within your infrastructure

Architecture Overview

MCP follows a client-server architecture where a host application can connect to multiple servers:

  • MCP Hosts: Programs like Claude Desktop, IDEs, or AI tools that want to access data through MCP
  • MCP Clients: Protocol clients that maintain 1:1 connections with servers
  • MCP Servers: Lightweight programs that expose specific capabilities through the standardized Model Context Protocol
  • Data Sources: Both local (files, databases) and remote services (APIs) that MCP servers can access

Core MCP Concepts

MCP servers can provide three main types of capabilities:

  • Resources: File-like data that can be read by clients (like API responses or file contents)
  • Tools: Functions that can be called by the LLM (with user approval)
  • Prompts: Pre-written templates that help users accomplish specific tasks

System Requirements

  • Python 3.10 or higher
  • MCP SDK 1.2.0 or higher
  • uv package manager

Getting Started

Installing uv Package Manager

On MacOS/Linux:

curl -LsSf https://astral.sh/uv/install.sh | sh

Make sure to restart your terminal afterwards to ensure that the uv command gets picked up.

Project Setup

  1. Create and initialize the project:
# Create a new directory for our project
uv init mcp-server
cd mcp-server

# Create virtual environment and activate it
uv venv
source .venv/bin/activate  # On Windows use: .venv\Scripts\activate

# Install dependencies
uv add "mcp[cli]" httpx
  1. Create the server implementation file:
touch main.py

Running the Server

  1. Start the MCP server:
uv run main.py
  1. The server will start and be ready to accept connections

Connecting to Claude Desktop

  1. Install Claude Desktop from the official website
  2. Configure Claude Desktop to use your MCP server:

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

{
    "mcpServers": {
        "mcp-server": {
            "command": "uv",  # It's better to use the absolute path to the uv command
            "args": [
                "--directory",
                "/ABSOLUTE/PATH/TO/YOUR/mcp-server",
                "run",
                "main.py"
            ]
        }
    }
}
  1. Restart Claude Desktop

Troubleshooting

If your server isn't being picked up by Claude Desktop:

  1. Check the configuration file path and permissions
  2. Verify the absolute path in the configuration is correct
  3. Ensure uv is properly installed and accessible
  4. Check Claude Desktop logs for any error messages

License

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

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

iflow_mcp_mcp_server_example-0.1.0.tar.gz (3.7 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

iflow_mcp_mcp_server_example-0.1.0-py3-none-any.whl (4.2 kB view details)

Uploaded Python 3

File details

Details for the file iflow_mcp_mcp_server_example-0.1.0.tar.gz.

File metadata

File hashes

Hashes for iflow_mcp_mcp_server_example-0.1.0.tar.gz
Algorithm Hash digest
SHA256 db404764285ff83bc2524c8eab0e80310fc4b035f91892b9863cf1448bb84ce4
MD5 167dc73bb853d76de0e545efcec2ea31
BLAKE2b-256 998b662253863aa615e6b971018d49ab25b52075cf57d2ae115b1e84f318836e

See more details on using hashes here.

File details

Details for the file iflow_mcp_mcp_server_example-0.1.0-py3-none-any.whl.

File metadata

File hashes

Hashes for iflow_mcp_mcp_server_example-0.1.0-py3-none-any.whl
Algorithm Hash digest
SHA256 290748030ad31272e82755aefe201545bc914aaf3902d04490669ff480422a78
MD5 c05a0a3147c64017406e51208c98ce28
BLAKE2b-256 7028dd86c3d5324444d5ccaac36b2e8e06c67f199d4223dc89a7f017cbce2e48

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page