Real-time global intelligence dashboard. AI-powered news aggregation, geopolitical monitoring, and infrastructure tracking in a unified situational awareness interface
MCP server for flight search tools
MCP Flight Search adds flight lookup tools to an MCP-compatible agent. It wraps SerpAPI Google Flights and registers tools for searching flights and checking server status.
People who want their agent to search flights without leaving the chat.
You can ask an agent to look up flights using airport codes and dates instead of doing the search yourself.
What it does
Flight search tool
Provides `search_flights_tool` for one-way and round-trip flight searches with origin, destination, outbound date, and optional return date.
Server status tool
Provides `server_status` so the client can check whether the MCP server is running.
SerpAPI integration
Uses SerpAPI Google Flights as the data source for flight results.
HTTP server entry point
Starts from `mcp-flight-search --connection_type http` or `python main.py --connection_type http`.
How to get it
- 1Run
# Install from PyPI pip install mcp-flight-search # Or install from the project directory (development mode) pip install -e .
- 2Start the MCP server
# Using the command-line entry point mcp-flight-search --connection_type http # Or run directly python main.py --connection_type http
- 3You can also specify a custom port
python main.py --connection_type http --port 5000
README
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/
├── mcp_flight_search/
│ ├── __init__.py # Package initialization and exports
│ ├── config.py # Configuration variables (API keys)
│ ├── models/
│ │ ├── __init__.py # Models package init
│ │ └── schemas.py # Pydantic models (FlightInfo)
│ ├── services/
│ │ ├── __init__.py # Services package init
│ │ ├── search_service.py # Main flight search logic
│ │ └── serpapi_client.py # SerpAPI client wrapper
│ ├── utils/
│ │ ├── __init__.py # Utils package init
│ │ └── logging.py # Logging configuration
│ └── server.py # MCP server setup and tool registration
├── main.py # Main entry point
├── pyproject.toml # Python packaging configuration
├── LICENSE # MIT License
└── 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.
Files in the repo
- .github
- mcp_flight_search
- .gitignore
- Dockerfile
- LICENSE
- main.py
- Makefile
- pyproject.toml
- README.md
- requirements-dev.txt
- requirements.txt
- smithery.yaml
Discussion (0)
Ask about usage, or say what you built with itSign in to join the discussion.
No comments yet. Be the first to say what this is good for.
More connectors
High-performance code intelligence MCP server. Indexes codebases into a persistent knowledge graph — average repo in milliseconds. 158 languages, sub-ms queries, 99% fewer tokens. Single static binary, zero dependencies.

Universal provider proxy for OpenAI Codex & Claude Code — use any LLM (Claude, Gemini, Grok, DeepSeek, Ollama…) with Codex CLI, App, SDK, and Claude Code
Git-native persistent memory for AI coding agents. Implements Google OKF v0.2 with sub-300µs in-memory BM25 search, embedded MCP server, and progressive disclosure. Slashes token bloat by 80% with zero external databases or dependencies. Built in pure Go.
Stop your AI from making things up — it proposes, deterministic tools decide, every claim checked against ground truth with evidence. Grounded facts and context survive resets. Reverse engineering is the proving ground. MCP server + CLI.
x64dbg-MCP Server is a native MCP (Model Context Protocol) plugin for x64dbg that exposes the debugger's full functionality over HTTP. Connect any MCP-compatible AI assistant and control x64dbg programmatically: set breakpoints, step through code, read memory, dump registers, and more. Built with Zig — zero dependencies, single-binary output, cros