Gemini Function Calling + Model Context Protocol(MCP) Flight Search

This project demonstrates how to use Google's Gemini 2.5 Pro with function calling capabilities to interact with the mcp-flight-search tool via Model Context Protocol (MCP). This client implementation shows how to:

1. Connect to a local MCP server process (mcp-flight-search) using stdio communication
2. Use natural language prompts with Gemini 2.5 Pro to search for flights (e.g., "Find flights from Atlanta to Las Vegas on 2025-05-05")
3. Let Gemini automatically determine the correct function parameters from the natural language input
4. Execute the flight search using the MCP tool
5. Display formatted results from the search

Features

• Natural language flight search using Gemini 2.5 Pro
• Automatic parameter extraction via function calling
• Integration with mcp-flight-search tool via stdio
• Formatted JSON output of flight results
• Environment-based configuration for API keys

Prerequisites

Before running this client, you'll need:

1. Python 3.7+
2. A Google AI Studio API key for Gemini
3. A SerpAPI key (used by the flight search tool)
4. The mcp-flight-search package installed

Dependencies

This project relies on several Python packages:

• google-generativeai: Google's official Python library for accessing Gemini 2.5 Pro and other Google AI models.

  • Provides the client interface for Gemini 2.5 Pro
  • Handles function calling capabilities
  • Manages API authentication and requests

• mcp-sdk-python: Model Context Protocol (MCP) SDK for Python.

  • Provides ClientSession for managing MCP communication
  • Includes StdioServerParameters for configuring server processes
  • Handles tool registration and invocation

• mcp-flight-search: A flight search service built with MCP.

  • Implements flight search functionality using SerpAPI
  • Provides MCP-compliant tools for flight searches
  • Handles both stdio and HTTP communication modes

• asyncio: Python's built-in library for writing asynchronous code.

  • Manages asynchronous operations and coroutines
  • Handles concurrent I/O operations
  • Required for MCP client-server communication

• json: Python's built-in JSON encoder and decoder.

  • Parses flight search results
  • Formats output for display
  • Handles data serialization/deserialization

Setup

1. Clone the Repository:

   git clone https://github.com/arjunprabhulal/mcp-gemini-search.git
   cd mcp-gemini-search
   

2. Install Dependencies:

   # Install required Python libraries
   pip install -r requirements.txt
   # Install the MCP flight search tool
   pip install mcp-flight-search
   

3. Set Environment Variables:

   export GEMINI_API_KEY="YOUR_GEMINI_API_KEY"
   export SERP_API_KEY="YOUR_SERPAPI_API_KEY"
   

   Replace the placeholder values with your actual API keys:

   • Get your Gemini API key from Google AI Studio
   • Get your SerpAPI key from SerpApi

Architecture

This project integrates multiple components to enable natural language flight search. Here's how the system works:

Component Interactions

1. User to Client

   • User provides natural language query (e.g., "Find flights from Atlanta to Las Vegas tomorrow")
   • Client script (client.py) processes the input

2. Client to MCP Server

   • Client starts the MCP server process (mcp-flight-search)
   • Establishes stdio communication channel
   • Retrieves available tools and their descriptions

3. Client to Gemini 2.5 Pro

   • Sends the user's query
   • Provides tool descriptions for function calling
   • Receives structured function call with extracted parameters

4. **Client

... View full README on GitHub