punitarani/fli — Google Flights MCP and Python Library

Description and use

**Brief Summary**
punitarani/fli is an open‑source Python library that implements Google Flights’ Model Context Protocol (MCP), providing a standard way for AI assistants to invoke real‑world tools and data sources. With over 2 400 stars and active recent commits, it offers a ready‑to‑use API/SDK/CLI for building MCP servers and integrating AI agents with flight‑search functionality and other services.

**Value**
The project abstracts the complexities of connecting AI agents to external tools behind a uniform protocol, enabling developers to plug in any MCP‑compatible service (e.g., flight search, booking, or custom data APIs) without rewriting glue code. This standardization accelerates AI‑agent development, reduces integration bugs, and fosters ecosystem interoperability.

**Practical Adoption Path**

1. **Prototype** – Install the Python package, run the provided CLI to spin up a local MCP server, and test against the Google Flights demo endpoints.
2. **Extend** – Implement custom handlers or adapters for your own services by following the library’s SDK guidelines and publishing the new endpoints as MCP routes.
3. **Deploy** – Containerize the server (Dockerfile is included), expose it via HTTPS, and register the endpoint with your AI assistant platform (e.g., LangChain, AutoGPT).
4. **Scale** – Leverage the library’s built‑in async support and monitoring hooks to run the server in production clusters (Kubernetes, Cloud Run, etc.).

**Production Readiness**
The repository shows strong OSS health signals: recent activity (last commit 2026‑05‑12), a large user base (≈2.4 k stars, 264 forks), clear documentation, and a well‑defined API surface. While the license and security posture still need a final audit, the codebase is mature, the Python ecosystem is stable, and the project is already being used in pilot deployments, making it a solid candidate for production‑grade pilots.

Problem

punitarani/fli helps connect AI assistants to real tools and data through a standard protocol.

Outcome

High for an OSS candidate: recent activity, adoption, and ecosystem signals are strong enough for a serious pilot.

Workflow

• connect AI agents to tools
• ship Model Context Protocol servers
• standardize integrations

Evidence

Google Flights MCP and Python Library

Source