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transit-agent MCP Server

Name: ruby-verma/transit-agent
Rating: 3.3 (1 reviews)
Author: ruby-verma

ruby-verma/transit-agent

Score: 57.4 Rank #6 MCP Server

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This guide demonstrates how to build an Agentic AI system using Google's Agent Development Kit (ADK) and the Model Context Protocol (MCP).

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Overview

ruby-verma/transit-agent is a Python MCP server licensed under Apache-2.0. This guide demonstrates how to build an Agentic AI system using Google's Agent Development Kit (ADK) and the Model Context Protocol (MCP).

Ranked #6 out of 100 indexed tools.

In the top 6% of all indexed tools.

Actively maintained with commits in the last week.

Ecosystem

Python Apache-2.0

Score Breakdown

Stars 15% 5

5 stars → early stage

Freshness 25% today

Last commit today → actively maintained

Issue Health 25% 50%

No issues filed → no history to score

Contributors 10% 2

2 contributors → solo project

Dependents 25% 0

No dependents → no downstream usage

npm Downloads N/A

PyPI Downloads N/A

Forks 0

Description Good

License Apache-2.0

Weights: Freshness 25% · Issue Health 25% · Dependents 25% · Stars 15% · Contributors 10% · How we score →

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Details

Stars: 5
Forks: 0
Open Issues: 0
Closed Issues: 0
Contributors: 2
Dependents: —
Language: Python
License: Apache-2.0
Last Commit: today

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From the README

# 🤖 Build an Autonomous Transit Agent with Google ADK & MCP

## Executive Summary
Today, we are moving beyond standard chatbots. In this hands-on lab, you will build a deterministic, multi-reasoning **Agentic AI** system using Google's Agent Development Kit (ADK) and the Model Context Protocol (MCP).

Instead of an AI that just generates text, you will build an orchestrator. Our Transit Agent will dynamically fetch live data, read business policies, and execute transactional workflows completely on its own. Finally, we will serve it through a beautiful web UI!

### The Tech Stack:

**- Google ADK**: The "Brain" - Orchestrates the ReAct (Reason + Act) loop using Gemini.

**- FastMCP**: The "Muscle" - Exposes your backend APIs and logic to the AI.

**- Docker**: For reliable, instant deployment.

## ☁️ Google Cloud Setup (Do This First!)
Before we look at the code, we need a workspace. Follow these steps to set up your Google Cloud environment:

### Step 1: Create a Google Cloud Project

Read full README on GitHub →