What is Remote Mcp Databricks For Gptdeepresearch

remote-mcp-databricks-for-gptdeepresearch is a prototype Minimal Command Protocol (MCP) interface designed for deep research and interactive data exploration using ChatGPT. It facilitates metadata exploration and SQL execution in a Databricks environment through a simplified search and fetch mechanism.

Use cases

Use cases include exploring data catalogs, executing SQL queries for data analysis, and integrating conversational interfaces with data platforms to enhance research capabilities.

How to use

To use remote-mcp-databricks-for-gptdeepresearch, set up the required environment variables such as DATABRICKS_WORKSPACE_URL, DATABRICKS_TOKEN, and DATABRICKS_WAREHOUSE_ID. Users can then interact with the MCP through commands that allow for metadata discovery and SQL query execution.

Key features

Key features include a search tool for discovering catalogs and tables, a fetch tool for executing SQL queries, SQL-aware search that interprets SQL-like inputs, and compatibility with the FastMCP framework for interactive use.

Where to use

remote-mcp-databricks-for-gptdeepresearch can be used in data analysis, business intelligence, and research environments where interactive data exploration and SQL querying are required.

Databricks Explorer MCP

Databricks Explorer MCP is a prototype Minimal Command Protocol (MCP) interface designed for deep research and interactive data exploration via ChatGPT. It allows metadata exploration and SQL execution in a Databricks environment using a simplified search/fetch mechanism.

⚠️ Note: This is a conceptual example designed for future integration with ChatGPT. While the structure is functional in a server-hosted context, ChatGPT cannot directly execute SQL or external fetch requests yet. This MCP serves as a foundation for such future capabilities.

💡 Purpose

This tool demonstrates how ChatGPT could eventually support real-time, SQL-driven research through simple commands. By building a unified abstraction for metadata discovery and SQL querying, it bridges the gap between conversational interfaces and data platforms like Databricks.

🚀 Features

• 🔍 Search Tool:

  • Discover catalogs, schemas, and tables via keyword search.
  • Detects SQL-like input and creates a placeholder query::<sql> ID.

• 💥 Fetch Tool:

  • Executes SQL queries using a fixed warehouse (only in external environments).
  • Returns Unity Catalog metadata for catalog/schema/table IDs.

• 🧠 SQL-aware Search:

  • Input beginning with sql: or SQL verbs (SELECT, INSERT, etc.) is interpreted as a query stub.

• 🌐 FastMCP Compatible:

  • Built on the FastMCP framework.
  • Supports SSE transport for interactive use.

⚙️ Environment Variables

🆔 ID Format Summary

🧩 Architecture

FastMCP
│
├── search(query) → metadata or SQL stub
└── fetch(id)     → SQL result or metadata

⚠️ ChatGPT Limitations

• ChatGPT cannot currently execute live SQL or access external APIs.
• This MCP is intended as a proof-of-concept and backend logic must be hosted separately.
• Useful for simulating integrations and planning future assistant capabilities.

📦 Installation & Run

pip install fastmcp requests
python server.py

Then access via:

http://localhost:8080/sse

🧪 Development Setup

Create and Activate a Virtual Environment

python3 -m venv .venv
source .venv/bin/activate

Install Dependencies

pip install -r requirements.txt

Run with MCP Inspector (Optional)

You can test the MCP interface using the Model Context Protocol Inspector:

npx @modelcontextprotocol/inspector@latest