Enterprise-MCP-Data-Agent
Provides secure SQL query and schema management capabilities for PostgreSQL databases through an MCP interface.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Enterprise-MCP-Data-Agentlist all tables in the database"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
P4: Enterprise MCP Data Agent (Local LLM + Secure PostgreSQL Integration)
An enterprise-grade, secure, and privacy-first Autonomous Data Agent built using the Model Context Protocol (MCP), FastMCP, LlamaIndex Workflows, and a localized LLaMA 3.2 model via Ollama. This agent acts as an automated SQL assistant that interacts securely with an on-premise PostgreSQL database using dynamic tool-calling capability, ensuring no database schemas are exposed raw to the external world.
🚀 Key Features
Model Context Protocol (MCP): Implements modern 2026 standardized client-server architecture (FastMCP) over Server-Sent Events (SSE).
Privacy-First Architecture: Utilizes local
llama3.2:1bfor zero-data leak enterprise compliances.Dynamic Tool Calling: Built-in SQL execution layer protecting database context via strict system prompting (
list_tables,read_data,add_data).Streamlit User Interface: A production-style, dynamic chat interface supporting streaming statuses of agent thoughts and backend tool invocations.
Related MCP server: PostgreSQL MCP Server
🛠️ Tech Stack
AI Framework: LlamaIndex (FunctionAgent Workflows)
MCP Server Framework: FastMCP (Python)
Database Driver: Psycopg3 (Modern PostgreSQL)
Local LLM Engine: Ollama (LLaMA 3.2 1B)
Frontend UI: Streamlit
📁 Project Structure
server.py- The standalone FastMCP server exposing database query and schema capabilities securely.agent_notebook.ipynb- Core testing and modular workflow pipeline using LlamaIndex client specs.app.py- Production-ready UI frontend wrapping the async agent loop.
🏃 How to Run
Step 1: Start the FastMCP Server
Ensure your local PostgreSQL database is up and matching the config, then run:
python server.py --server_type sse --port 8000This server cannot be deployed
Maintenance
Related MCP Connectors
Query PostgreSQL databases in plain English — LLM-generated, safety-validated SQL.
Deterministic safety, correctness & cost gate that vets Postgres SQL before your AI agent runs it.
Query your Postgres from ChatGPT or Claude without exposing the database or handing over credentials. Run npx boltschema connect next to your database and it dials out over HTTPS — no inbound firewall rule, no open port, works with localhost and VPC-private databases. Read-only is enforced by a SQL guard, a Postgres READ ONLY transaction, and a scoped role generated for you.
Ask data questions in natural language. Get SQL, insights, and charts from your databases.
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