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-0.1.1.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-0.1.1-py3-none-any.whl (4.2 kB view details)

Uploaded Python 3

File details

Details for the file iflow_mcp_mcp_server_example-0.1.1.tar.gz.

File metadata

File hashes

Hashes for iflow_mcp_mcp_server_example-0.1.1.tar.gz
Algorithm Hash digest
SHA256 2239fdd29476dc44145cb06c140eedb4e2a38cbe482380c2f38b216ef1425429
MD5 03f465534d6e2ccf39e13bae71aefcd2
BLAKE2b-256 2887811a11d0550679194c45fb9b3ecb8471b2bfb61782726293c516325f2835

See more details on using hashes here.

File details

Details for the file iflow_mcp_mcp_server_example-0.1.1-py3-none-any.whl.

File metadata

File hashes

Hashes for iflow_mcp_mcp_server_example-0.1.1-py3-none-any.whl
Algorithm Hash digest
SHA256 e8702fb47399c6c73f5a237a3a5cf6ad624318706b99e227332f87dfb8724a60
MD5 08dc7f152179300db2fd2b19d172c650
BLAKE2b-256 2dd3a1e890815ec2fcd83bf591b480df03283a7b6a12f7edb479aab42af8c27c

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