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Diluksha-Upeka

Enterprise Data MCP Server

by Diluksha-Upeka
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Enterprise Data Agent 🗄️

A natural-language interface to an enterprise employee database, powered by Groq LLM tool-calling and a local SQLite database, with a Streamlit frontend and a Model Context Protocol (MCP) server backend.

Architecture

flowchart TD
    %% Styling
    classDef client fill:#2d3748,stroke:#94a3b8,stroke-width:1px,color:#f8fafc,rx:10px
    classDef core fill:#0f172a,stroke:#3b82f6,stroke-width:2px,color:#eff6ff,rx:6px
    classDef db fill:#064e3b,stroke:#10b981,stroke-width:2px,color:#ecfdf5,rx:6px
    classDef ai fill:#312e81,stroke:#8b5cf6,stroke-width:2px,color:#eef2ff,rx:6px

    %% Nodes
    User(["👤 User Browser"]):::client
    ExtAI(["🤖 MCP Client (Claude, Cursor)"]):::client

    App["⚡ Streamlit App"]:::core
    Server["🔌 MCP Server"]:::core
    DBLayer["⚙️ Shared DB Layer"]:::core

    Groq{"🧠 Groq LLM"}:::ai
    SQLite[("💾 SQLite Database")]:::db

    %% Connections
    User <-->|"Chat Interface"| App
    App <-->|"Prompts & Tools"| Groq
    App -->|"Execute Tools"| DBLayer

    ExtAI <-->|"MCP / stdio"| Server
    Server -->|"Execute Tools"| DBLayer

    DBLayer <-->|"SQL Operations"| SQLite

Quick Start

1. Prerequisites

2. Clone & Set Up

git clone <your-repo-url>
cd mcp_project

# Create and activate a virtual environment
python -m venv venv
venv\Scripts\activate        # Windows
# source venv/bin/activate   # macOS/Linux

# Install dependencies
pip install -r requirements.txt

3. Configure Environment

Create a .env file in the project root (F:\PROJECTS\MCP\.env):

GROQ_API_KEY=gsk_your_key_here
GROQ_MODEL=llama-3.3-70b-versatile   # optional, this is the default

4. Initialize the Database

# From the project root (F:\PROJECTS\MCP\)
sqlite3 enterprise_data.db < schema.sql

5. Run the App

# From F:\PROJECTS\MCP\
streamlit run mcp_project/app.py

Open http://localhost:8501 in your browser.

Available Tools

The LLM can call these tools automatically based on your query:

Tool

Description

get_active_users

List all active users and their roles

add_user

Add a new user (name + role)

deactivate_user

Deactivate an active user by name

get_user_stats

Get total, active, and inactive counts

search_users

Search users by name fragment

Example Queries

  • "Who are our active users?"

  • "Add Jane Doe as a Manager"

  • "How many users do we have in total?"

  • "Search for users named Alice"

  • "Deactivate Bob Jones"

  • "Show me user statistics"

Running the MCP Server (Standalone)

server.py exposes the same tools via the Model Context Protocol. It supports both local stdio connections and an HTTP-based sse Transport!

It now exposes:

  • Tools: DB query and update operations

  • Resources: Exposes schema.sql over MCP

  • Prompts: Provides pre-configured prompts like hr-assistant

To run locally for AI desktop apps (like Claude Desktop / Cursor):

python mcp_project/server.py --transport stdio

To run as an HTTP SSE server on port 8000:

python mcp_project/server.py --transport sse --port 8000

Project Structure

F:\PROJECTS\MCP\
├── .env                    # ← API key (never commit this!)
├── .gitignore
├── requirements.txt
├── schema.sql              # DB schema — use to recreate the database
├── enterprise_data.db      # SQLite database (gitignored)
└── mcp_project\
    ├── app.py              # Streamlit frontend
    ├── server.py           # MCP stdio server
    └── db.py               # Shared database access layer

Security Notes

  • Never commit your .env file. The .gitignore excludes it.

  • Never commit enterprise_data.db. Use schema.sql to recreate it.

  • Rotate your Groq API key at console.groq.com if it has ever been exposed.

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