🚀 MCP-Powered Streamlit Dashboard with Ollama + PostgreSQL

A modern data analysis tool that lets you:

• Query your PostgreSQL database with natural language

• Run SQL manually or generate it using LLMs (Ollama)

• Analyze data with charts, statistics, and export to CSV

🧠 Prompt-to-SQL Flow

graph TD
    A[🧑 User types a data question] --> B[Streamlit sends prompt to Ollama API]
    B --> C[Ollama generates SQL query as text]
    C --> D[Streamlit extracts the SQL]
    D --> E[Streamlit sends SQL to MCP server]
    E --> F[MCP executes query on PostgreSQL]
    F --> G[Results returned to Streamlit]
    G --> H[📊 Results shown + Chart + CSV Export]

Related MCP server: PostgreSQL MCP AI Explorer

📦 Architecture

• Streamlit – UI + charting

• MCP (FastMCP) – Tools/resources for SQL query and table listing

• PostgreSQL – Stores your company data

• Ollama – LLM that translates natural language prompts to SQL

📌 Features

✅ Natural language → SQL
✅ Charting (bar/line/time series)
✅ CSV download
✅ Statistical summary
✅ Prompt explainability with raw output
✅ Auto-detect date/time fields
✅ LLM integration with llama3 (configurable)

🛠️ Getting Started

git clone <this-repo>
cd postgres-mcp-server
docker-compose up --build

• Access UI: http://localhost:8501

• MCP API: http://localhost:3333/mcp

⚙️ Env Configuration

MCP_API_URL=http://mcp-server:3333/mcp
OLLAMA_URL=http://ollama:11434/api/generate

📤 Prompt Example

“List departments with average salary > 50000”

👉 Translated to SQL:

SELECT department, AVG(salary) FROM employees GROUP BY department HAVING AVG(salary) > 50000;

Why is this a good use case for MCP?

🔗 MCP makes it dead simple to expose structured tools like SQL queries to LLMs. 🎯 Agents can discover and call your tools without hardcoding logic. 💬 You get the best of both worlds — interpretability, flexibility, and control.

Whether you're building internal tools, research dashboards, or intelligent agents — this pattern is reusable, secure, and 100% local.

📄 License

MIT