arjunprabhulal mcp flight search
by arjunprabhulal
MCP Server implementation for the Model Context Protocol (MCP) enabling AI tool usage - Realtime Flight Search
What is arjunprabhulal mcp flight search
MCP Flight Search
A flight search service built with Model Context Protocol (MCP). This service demonstrates how to implement MCP tools for flight search capabilities.
What is Model Context Protocol?
The Model Context Protocol (MCP) is a standard developed by Anthropic that enables AI models to use tools by defining a structured format for tool descriptions, calls, and responses. This project implements MCP tools that can be used by Claude and other MCP-compatible models.
Installation
# Install from PyPI
pip install mcp-flight-search
# Or install from the project directory (development mode)
pip install -e .
Usage
Start the MCP server:
# Using the command-line entry point
mcp-flight-search --connection_type http
# Or run directly
python main.py --connection_type http
You can also specify a custom port:
python main.py --connection_type http --port 5000
Environment Variables
Set the SerpAPI key as an environment variable:
export SERP_API_KEY="your-api-key-here"
Features
- MCP-compliant tools for flight search functionality
- Integration with SerpAPI Google Flights
- Support for one-way and round-trip flights
- Rich logging with structured output
- Modular, maintainable code structure
MCP Tools
This package provides the following Model Context Protocol tools:
-
search_flights_tool
: Search for flights between airports with parameters:origin
: Departure airport code (e.g., ATL, JFK)destination
: Arrival airport code (e.g., LAX, ORD)outbound_date
: Departure date (YYYY-MM-DD)return_date
: Optional return date for round trips (YYYY-MM-DD)
-
server_status
: Check if the MCP server is running
Project Structure
mcp-flight-search/
โโโ mcp_flight_search/
โ โโโ __init__.py # Package initialization and exports
โ โโโ config.py # Configuration variables (API keys)
โ โโโ models/
โ โ โโโ __init__.py # Models package init
โ โ โโโ schemas.py # Pydantic models (FlightInfo)
โ โโโ services/
โ โ โโโ __init__.py # Services package init
โ โ โโโ search_service.py # Main flight search logic
โ โ โโโ serpapi_client.py # SerpAPI client wrapper
โ โโโ utils/
โ โ โโโ __init__.py # Utils package init
โ โ โโโ logging.py # Logging configuration
โ โโโ server.py # MCP server setup and tool registration
โโโ main.py # Main entry point
โโโ pyproject.toml # Python packaging configuration
โโโ LICENSE # MIT License
โโโ README.md # Project documentation
Author
For more articles on AI/ML and Generative AI, follow me on Medium: https://medium.com/@arjun-prabhulal
License
This project is licensed under the MIT License - see the LICENSE file for details.
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Frequently Asked Questions
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, providing a standardized way to connect AI models to different data sources and tools.
What are MCP Servers?
MCP Servers are lightweight programs that expose specific capabilities through the standardized Model Context Protocol. They act as bridges between LLMs like Claude and various data sources or services, allowing secure access to files, databases, APIs, and other resources.
How do MCP Servers work?
MCP Servers follow a client-server architecture where a host application (like Claude Desktop) connects to multiple servers. Each server provides specific functionality through standardized endpoints and protocols, enabling Claude to access data and perform actions through the standardized protocol.
Are MCP Servers secure?
Yes, MCP Servers are designed with security in mind. They run locally with explicit configuration and permissions, require user approval for actions, and include built-in security features to prevent unauthorized access and ensure data privacy.
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