🚀 MCP Tools Server with BigQuery Integration

This project demonstrates how to build and serve tools for Model Context Protocol (MCP) using Python, FastMCP, Google BigQuery, and Starlette. The tools include querying BigQuery datasets, retrieving schema information, and listing tables — all designed to plug into AI systems for dynamic tool usage.

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🧠 What is MCP?

Model Context Protocol (MCP) is a protocol that allows language models to interact with external tools and APIs in a structured, declarative manner. With MCP, you can expose real-world data operations like database queries, web APIs, or even file systems to be safely invoked by LLMs.

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📦 Features

• ✅ Easy integration with Google BigQuery
• ✅ Expose tools like get_table_list, get_schema, run_query
• ✅ Real-time interaction over SSE using Starlette
• ✅ Configurable via .env and service account

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🛠️ Tools Implemented

Tool Name	Description
get_list_of_tablename	Lists all tables in the specified BigQuery dataset
get_table_information	Retrieves the schema of a specified table
excute_qury_and_get_results	Executes a custom SQL query and returns results

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🔧 Setup

1. Clone the repo

git clone https://github.com/ARULJERALD8682/MCP-Tools.git
cd MCP-Tools 

2. Set up environment

Create a .env file with:

DATASET_NAME=your_dataset_name

Place your BigQuery service_account.json in the project root.

3. Install dependencies

pip install -r requirements.txt

4. Run the server

python main.py --host 0.0.0.0 --port 8081