About Practice
Practice is an MCP server published by Mustafaguzel2 in the AI category: a Model Context Protocol (MCP) practice project demonstrating server/client implementation with user management tools, resources, and prompts. Built with TypeScript, Node.js v24, and Google Gemini AI integration.
The repository has 1 stars and 0 forks, with the last commit 8 months ago. Six months or more without a commit doesn't mean the server is broken, but check the open issues before depending on it in production.
Install
git clone https://github.com/Mustafaguzel2/MCP-PracticeThis server has no ConduID identity, so agent calls to it are not receipted. Pin the version you install and review the source before granting it credentials.
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Security checks
Static checks derived from the repository 5 days ago. Heuristics on the source, not an audit.
- ✓README presentThe repository ships documentation.
- !License declaredNo license file. Legally you have no right to use it in a product.
- !Tests presentNo tests found. Behaviour changes may go unnoticed between versions.
- ✓Dependencies pinnedA lockfile pins dependency versions.
- ✓No dynamic code executionSource scan found no eval, dynamic Function, or shell execution.
- !Scoped permissionsDoesn't declare a permission scope. Assume it can do anything its process can.
README
MCP Practice Project
A Model Context Protocol (MCP) server and client implementation for learning and practicing MCP development. This project demonstrates how to build an MCP server that exposes resources, tools, and prompts, along with an interactive CLI client to interact with them.
Features
MCP Server (src/server.ts):
- Resources: Query user data via
users://allandusers://{userId}/profile - Tools:
create-user- Create a user with explicit fields (name, email, address, phone)create-random-user- Generate a random user using LLM sampling
- Prompts:
generate-fake-user- Generate fake user data based on a name
MCP Client (src/client.ts):
- Interactive CLI for testing MCP server capabilities
- Query tools, resources, and prompts
- Natural language query interface using Google Gemini AI
Tech Stack
- Runtime: Node.js v24.10.0
- Language: TypeScript (strict mode, ES modules)
- Framework: Model Context Protocol SDK (
@modelcontextprotocol/sdk) - AI: Google Gemini API (
@ai-sdk/google) - Validation: Zod
- Storage: JSON file (
src/data/users.json)
Quick Start
# Install dependencies
npm install
# Build the server
npm run server:build
# Run the server (for MCP Inspector)
npm run server:inspect
# Run the interactive client
npm run client:dev
Project Structure
src/
├── server.ts # MCP server implementation
├── client.ts # MCP client CLI
└── data/
└── users.json # JSON-based user database
Development
This is a practice/learning project for understanding MCP development patterns. The server uses StdioServerTransport for communication, and data is stored in a simple JSON file (not production-ready).
README fetched from the repository 5 days ago. The original is authoritative.