The Concept: Conversational BI
The goal of this project is to eliminate the friction between "asking a question" and "getting the data." By exposing Metabase's powerful analytics engine through the MCP standard, I've enabled a workflow where data exploration happens at the speed of thought, directly within the developer's chat interface.
Key Capabilities
- Natural Language Analytics: Seamlessly translates conversational prompts into executable SQL queries against your Metabase-connected databases.
- Autonomous Discovery: Allows AI agents to list databases, inspect complex schemas, and understand field metadata with built-in pagination support.
- Resource Management: Extends beyond simple querying to allow the management of Metabase Cards (questions), Collections, and Dashboards.
- Unified Interface: Supports multiple transport layers including STDIO for local IDE integration and SSE/HTTP for remote server architectures.
Technical Execution
- Core Framework: Built using FastMCP for high-level protocol orchestration and robust error handling.
- Language: Python 3.12+ with strict typing and modern asynchronous patterns.
- Tooling Ecosystem: Leverages uv for high-performance dependency management and Ruff/Mypy for industrial-grade code quality.
- Security: Implements flexible authentication supporting both API Key and session-based flows.
This project represents my focus on building "Connective Tissue"—the software layers that enable advanced AI models to safely and effectively navigate real-world enterprise data.