MCP Servers

A collection of Model Context Protocol servers, templates, tools and more.

M
Multi Agent App With MCP Toolbox For Alloydb Adk

A generative AI agent built with the Google ADK and Gemini 2.5 Flash. This agent can process natural language queries regarding hardware orders, inventory status, and renovation logistics.

Created 5/9/2026
Updated about 3 hours ago
Repository documentation and setup instructions

A smarter way to manage renovations using the Agent Development Kit (ADK), Gemini 2.5 Flash, and MCP Toolbox for AlloyDB

System Architecture & Theory

When an application utilizes multiple agents working together—each independently knowledgeable and responsible for a specific focus area—it becomes a Multi-Agent System. Using the ADK framework, we can compose these agents into a hierarchy to achieve complex goals with enhanced modularity and specialization.

What This Project Builds This application coordinates three specialized agents to handle the lifecycle of a kitchen renovation:

Renovation Proposal Agent: Generates detailed renovation proposal documents and project timelines.

Permits & Compliance Agent: Navigates local regulations and ensures all renovation tasks meet legal requirements.

Order Status Agent (Data-Grounded): Checks the status of materials (e.g., "Cement Bags") by querying an order management database.

Implementation: Uses the MCP Toolbox for AlloyDB to bridge the LLM with a live SQL database for real-time status retrieval.

✨ Key Features Specialized Specialization: Each agent is "expert-coded" for a specific domain, preventing model drift.

Root Routing: A central orchestrator routes user queries to the correct sub-agent.

Database Grounding: Real-world truth is pulled from AlloyDB via the Model Context Protocol (MCP).

Cloud Native: Fully optimized for Google Cloud Vertex AI and Gemini 2.5 Flash.

🚀 Getting Started Prerequisites Google Cloud Project with Vertex AI enabled.

Python 3.10+

ADK (Agent Development Kit) installed.

Setup Clone the Repository:

Bash git clone https://github.com/YOUR_USERNAME/renovation-mas-agent.git cd renovation-mas-agent Environment Configuration: Create a .env file in the root directory:

Plaintext GOOGLE_CLOUD_PROJECT=your-project-id GOOGLE_CLOUD_LOCATION=us-central1 MODEL_NAME=gemini-2.5-flash GOOGLE_GENAI_USE_VERTEXAI=True ```

  1. Install Dependencies:
    pip install -r requirements.txt
    ```

4.  **Launch the System:**
    *   **CLI Mode:** `adk run .`
    *   **Web UI Mode:** `adk web` (Preview on port 8000 in Cloud Shell)

---

## 📈 Designing Your Own MAS: Things to Keep in Mind
*   **Reasoning for Specialization:** Always ask: "Why do I need a specific sub-agent for this task?" Work out the specialization before writing code.
*   **Routing Logic:** Choose a routing type (Hierarchical, Sequential, or Joint) that suits your specific workflow.
*   **State Management:** Ensure your Root Agent can maintain context across multiple sub-agent transitions.

---

### Pro-Tip for your GitHub
Since you mentioned using `adk web`, definitely take a screenshot of the **Trace** view showing the agent calling the AlloyDB tool and paste it right under the **What This Project Builds** section. It’s the "proof" that your autonomous system is actually working!
Quick Setup
Installation guide for this server

Install Package (if required)

uvx multi-agent-app-with-mcp-toolbox-for-alloydb-adk

Cursor configuration (mcp.json)

{ "mcpServers": { "mabasabee603163-multi-agent-app-with-mcp-toolbox-for-alloydb-adk": { "command": "uvx", "args": [ "multi-agent-app-with-mcp-toolbox-for-alloydb-adk" ] } } }