Enterprise Tools Server

An MCP-based enterprise tools server that exposes company knowledge search and employee database lookup as callable tools. The project demonstrates how an AI client such as Cursor, Claude Desktop, or a custom Python MCP client can discover tools, call them through the Model Context Protocol, and return structured results.

This project currently includes a document-search tool backed by an existing RAG API and a SQLite query tool for employee data.

Features

• MCP server built with FastMCP
• search_documents tool for company knowledge lookup through a RAG API
• query_database tool for SQLite employee database queries
• Local stdio MCP client for testing tool discovery and execution
• Cursor MCP configuration example
• Environment-based configuration for the RAG API URL
• JSON-formatted database responses
• Small sample employee database for quick testing

Workflow

User / MCP Client
      |
      v
MCP Server
      |
      +--> search_documents --> RAG API --> Company Knowledge
      |
      +--> query_database ----> SQLite ----> Employee Records
      |
      v
Tool Response

Screenshots

Cursor MCP Client Integration

Database Query Tool

Company Document Search Tool

Project Structure

.
|-- db.py                 # Creates and seeds the sample employees SQLite database
|-- employees.db          # Local SQLite database generated for testing
|-- mcp_client.py         # CLI MCP client using stdio transport
|-- mcp_config.json       # MCP server configuration example
|-- mcp_server.py         # FastMCP server with enterprise tools
|-- requirements.txt      # Python dependencies
|-- README.md             # Project documentation
`-- Screenshots/          # Project screenshots

Tools

search_documents

Searches company documents by sending the user query to a RAG API.

Input:

{
  "query": "what is refund duration?"
}

Output:

Context: ...
Sources: ...

query_database

Runs a SQL query on the local SQLite employee database and returns rows as formatted JSON.

Input:

{
  "sql": "select * from employees"
}

Output:

[
  {
    "id": 1,
    "name": "Harry",
    "salary": 50000,
    "department": "AI"
  }
]

Setup

Clone the repository:

git clone https://github.com/Harry-GenAI/05-mcp-enterprise-tool-server.git
cd 05-mcp-enterprise-tool-server

Create and activate a virtual environment:

python -m venv venv
venv\Scripts\activate

Install dependencies:

pip install -r requirements.txt

Create a .env file:

RAG_URL=http://localhost:8000/rag

Create the sample database:

python db.py

Run MCP Client

Use the local CLI client to start the MCP server over stdio, list available tools, and call one of them:

python mcp_client.py

Example database query:

Enter tool name: query_database
Enter SQL Query: select * from employees

Example document search:

Enter tool name: search_documents
Enter search query: what is refund duration?

Cursor MCP Configuration

Add this configuration to your Cursor MCP settings:

{
  "mcpServers": {
    "enterprise_tools": {
      "command": "python",
      "args": ["mcp_server.py"]
    }
  }
}

After configuration, Cursor can discover and call:

• search_documents
• query_database

Tech Stack

• Python
• Model Context Protocol
• FastMCP
• SQLite
• Requests
• python-dotenv
• Cursor MCP integration

Future Upgrades

• Add SQL safety validation for read-only database access
• Support more enterprise tools such as CRM, HR, tickets, and policy lookup
• Add FastAPI wrapper for HTTP-based testing
• Return richer structured responses from the RAG tool
• Add authentication for protected internal tools
• Add automated tests for MCP tool calls

Note

This project is for learning and demonstration. Keep .env, virtual environments, production databases, private documents, and secrets out of GitHub.