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., "@Kapruka MCP Server search for organic tea products"
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.
Python MCP server that wraps the Kapruka.com REST API and exposes it as tools for LLMs and third-party MCP clients.
# 1. Create and activate a virtual environment
python -m venv .venv
source .venv/bin/activate # Windows: .venv\Scripts\activate
# 2. Install dependencies
pip install -e ".[dev]"
# 3. Configure environment
cp .env.example .env
# Edit .env with your Kapruka API URL and keyRelated MCP server: mcp-server-for-apache-ofbiz
# Start the MCP server (streamable HTTP, default port 8000)
python cli.py server
# Start with stdio transport (for use with MCP Inspector)
python cli.py server --stdio
# Health-check the Kapruka REST API
python cli.py ping
# List registered MCP tools
python cli.py toolsnpx @modelcontextprotocol/inspector python cli.py server --stdiosrc/
server.py # FastMCP server entry point
tools/ # One module per tool group (products, orders, …)
api/
client.py # Async httpx client + error handling
config/
settings.py # Env-based configuration
tests/ # pytest test suite
cli.py # Developer CLIpytestThis server cannot be deployed
An MCP server that provides an API to LLMs to manage their JumpCloud resources.
Nifty's MCP server — exposes tasks, projects, messages, and files as tools for AI agents.
MCP server exposing the Backtest360 engine API as tools for AI agents.
The Mercado Pago MCP Server implements the Model Context Protocol to provide AI agents and LLMs with access to Mercado Pago's APIs and tools within compatible development environments. It acts as an intermediary that translates Mercado Pago resources into executable functions (tools) that AI applications can invoke to perform actions and automate flows. The server simplifies integration, enables using documentation to implement or improve code, and optimizes operations through natural language interactions without manual implementations.
