mcp-server

The MCP Server is a generic, modular server designed to implement the Model Context Protocol (MCP). It provides a framework for controlling and interacting with various models through a standardized API. With features like dynamic module loading, core model management, and a simple configuration system, it facilitates easy extension and integration with real AI model providers.

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Installation
Difficulty
Intermediate
Estimated Time
10-20 minutes
Requirements
Node.js 18.x or higher
pnpm 10.x or higher

Installation

Installation

Prerequisites

Please specify required software and versions:
Node.js: 18.x or higher
pnpm: 10.x or higher

Installation Steps

1. Clone Repository

bash
git clone https://github.com/profullstack/mcp-server.git
cd mcp-server

2. Install Dependencies

bash
pnpm install

3. Start Server

bash
pnpm start

4. Start in Development Mode (with auto-reload)

bash
pnpm dev

Troubleshooting

Common Issues

Issue: Server won't start Solution: Check Node.js version and reinstall dependencies.

Configuration

Configuration

Basic Configuration

Copy Sample Environment File

bash
cp sample.env .env

Edit Environment File

bash
nano .env
Add the necessary API keys:

OPENAI_API_KEY=your_openai_api_key_here
STABILITY_API_KEY=your_stability_api_key_here
ANTHROPIC_API_KEY=your_anthropic_api_key_here

Advanced Configuration

Security Settings

Store API keys in environment variables or secure configuration files
Set appropriate file access permissions

Performance Tuning

Configure timeout values
Limit concurrent executions

Examples

Examples

Basic Usage

Programmatic Usage

javascript
// JavaScript example (Node.js)
const { MCPClient } = require('@modelcontextprotocol/client');

const client = new MCPClient();
await client.connect();

// Execute tool
const result = await client.callTool('toolName', {
  parameter1: 'value1',
  parameter2: 'value2'
});

console.log(result);

Use Cases

Building a text generation application using AI models
Developing an art generation tool utilizing image generation models
Implementing a chatbot with speech recognition capabilities
Creating a data analysis platform that integrates multiple AI models