Mcp-ChatBI

What is Mcp-ChatBI?

Mcp-ChatBI is a data analysis agent solution built on the MCP architecture, designed to address common ChatBI problems such as data accuracy, multi-metric queries, and automated analysis chains. It runs on Python 3.10–3.12 and is deployed via an API backend and a Gradio web interface.

How to use Mcp-ChatBI?

Clone the repository, install dependencies with pip3 install -r requirements.txt, configure your Qwen model API key and model name in config/config.toml, then start the API with python3 main-api.py and the Gradio interface with python3 main-webui.py.

Key features of Mcp-ChatBI

• Text2Tools approach for 100% data accuracy
• Multi-metric query in a single conversation
• Automated data analysis chain via model reasoning
• Supports Headless BI services or custom APIs
• Gradio web UI for easy interaction

Use cases of Mcp-ChatBI

• Ensuring absolute data accuracy in business intelligence reporting
• Querying multiple metrics (e.g., revenue, users, conversion) in one dialogue
• Automating complex analytical workflows like comparative analysis or root cause attribution

FAQ from Mcp-ChatBI

How does Mcp-ChatBI guarantee data accuracy?

Instead of Text2SQL or Text2Code, it uses a Text2Tools pattern where tools are Headless BI services or APIs, eliminating model hallucination.

Can I query multiple metrics in one conversation?

Yes. The model automatically decomposes the request, plans a task order, and calls the appropriate tools sequentially, returning all results together.

Which large language model does Mcp-ChatBI support?

It is configured to use Qwen models (e.g., qwen-max) via the config/config.toml file, where you set the API key and model name.

What are the deployment requirements?

Python 3.10 to 3.12 and installation of dependencies from requirements.txt. No GPU is explicitly required; it relies on model API calls.

Is Mcp-ChatBI open-source?

Yes. The project is available on GitHub and can be freely cloned and deployed.