Which integrations are available for this server?

Enables AI agents to query a PostgreSQL database through a secure layer of predefined tools, allowing for the retrieval of employee information, project statuses, and issue priorities without exposing database credentials or allowing direct SQL execution.

How do I use MCP Dummy DB Integration?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@MCP Dummy DB Integration list all employees in the AI department" 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.

MCP Dummy DB Integration and Data Retrieval POC

Executive Summary

This project demonstrates a secure, production-ready implementation of the Model Context Protocol (MCP) as a connector layer between AI agents and PostgreSQL databases. The solution enables natural language queries without exposing database credentials to the LLM.

Key Achievement: LLM cannot access database directly - only through predefined MCP tools.

Architecture Overview

┌────────────────────────────────────────────────────────┐ │ USER QUERY │ │ "Fetch employees in AI department" │ └───────────────────┬────────────────────────────────────┘ │ ▼ ┌────────────────────────────────────────────────────────┐ │ PLANNER AGENT (LLM) │ │ ✓ Natural Language Understanding │ │ ✗ NO database credentials │ │ Output: {"tool": "get_employees_by_department", │ │ "parameters": {"department": "AI"}} │ └───────────────────┬────────────────────────────────────┘ │ ▼ ┌────────────────────────────────────────────────────────┐ │ EXECUTOR AGENT │ │ ✓ Validates tool request │ │ ✓ Maps to allowed operations only │ │ ✗ Cannot execute arbitrary SQL │ └───────────────────┬────────────────────────────────────┘ │ ▼ ┌────────────────────────────────────────────────────────┐ │ MCP TOOLS LAYER (Sandbox) │ │ ✓ get_employees_by_department("AI") │ │ ✓ get_projects_by_status("Completed") │ │ ✓ get_issues_by_priority("High") │ │ ✗ Cannot run arbitrary SQL │ └───────────────────┬────────────────────────────────────┘ │ ▼ ┌────────────────────────────────────────────────────────┐ │ DATABASE CONNECTION (Secure) │ │ ✓ Credentials in environment variables │ │ ✓ Only parameterized queries (SQL injection safe) │ └───────────────────┬────────────────────────────────────┘ │ ▼ ┌────────────────────────────────────────────────────────┐ │ RESULT TO USER │ │ [Secure data retrieval via MCP] │ └────────────────────────────────────────────────────────┘

Security Features

Feature	With MCP
DB Credentials	Secure in .env ✅
SQL Access	Predefined tools only ✅
Attack Surface	Limited operations only ✅
Audit Trail	Full logging ✅
Connection Pool	Yes ✅

Project Structure

MCP Task/ ├── demo_agent_workflow.py # Main Entry point (Agentic Workflow) ├── main.py # Interactive CLI entry point ├── .env # Database credentials (not in git) ├── README.md # This file ├── database/ │ └── db_executor.py # Database connection & queries ├── mcp/ │ └── tools.py # MCP tool definitions ├── agents/ │ ├── orchestrator.py # Manages the Agentic Workflow │ ├── planner_agent.py # Query planning agent (w/ Robust Fallback) │ ├── executor_agent.py # Query execution agent (Safe validation) │ ├── reasoner_agent.py # Result explanation agent │ └── llm_provider.py # LLM Interface ├── utils/ │ └── serializer.py # Custom JSON serialization └── datas_insert/ └── sample_data.sql # Sample database setup (PostgreSQL)

Setup Instructions

Prerequisites

Python 3.8+
PostgreSQL 12+
Ollama (running locally) or other LLM provider
pip packages: psycopg2, python-dotenv

Installation

Clone the repository
Create a virtual environment:
python -m venv venv venv\Scripts\activate
Install dependencies:
pip install -r requirements.txt
Configure .env:
DB_HOST=localhost DB_PORT=5432 DB_USER=postgres DB_PASSWORD=your_password DB_NAME=mcp_db
Initialize database:
psql -U postgres -d mcp_db -f datas_insert/sample_data.sql

Running the Demo

python demo_agent_workflow.py

Running the Interactive CLI

python main.py

Agentic Workflow Design

This project uses a multi-agent secure architecture:

Orchestrator: The central brain that manages the lifecycle of a request.
Planner Agent:
- Role: Analyzes the user query and selects the appropriate MCP tool.
- Robustness: Uses a Dual-Layer Strategy.
  - Layer 1: Tries to parse the LLM's JSON output.
  - Layer 2: If LLM output is malformed (common with small models), it falls back to a deterministic keyword extraction strategy to ensure the query is always answered correctly.
Executor Agent:
- Role: Validates the plan and executes the cached tool.
- Safety: Ensures only allowed tools are called and handles parameter types safely.
MCP Tool Layer: A sandboxed layer that prevents direct SQL access.
Reasoner Agent: (Optional) Summarizes the raw data into a human-readable answer.

How MCP Works as a Connector Layer

User Query → "Fetch employee details where department is AI"
Planner Agent → LLM interprets query, creates plan without DB access
MCP Tools → Translates plan to allowed operations (get_employees_by_department)
Secure Execution → Only predefined MCP tools can access the database
Result → Data returned to user

Security Benefit: The LLM never sees or uses database credentials directly.

MCP Tools Available

get_employees_by_department(department) - Fetch employees by department
get_projects_by_status(status) - Fetch projects by status
get_issues_by_priority(priority) - Fetch issues by priority

Example Queries

"Fetch employee details where department is AI"
"Show all projects with status completed"
"List all high priority issues"

Created by:

MCP Dummy DB Integration