MCP Base - A Generic Model Context Protocol Framework

This folder contains a general-purpose base implementation of the Model Context Protocol (MCP) for building AI-powered applications. It provides a standardized way to create MCP servers and clients that can be used to integrate LLMs into your applications.

📋 Features

- Standardized MCP Server: A base server implementation with support for HTTP and stdio transports
- Generic MCP Client: A client for connecting to any MCP server
- Ollama Integration: Ready-to-use services for generating embeddings and text with Ollama
- Supabase Integration: Built-in support for Supabase vector database
- Modular Design: Clearly organized structure for resources, tools, and prompts
- Sample Templates: Example implementations to help you get started quickly

🛠️ Directory Structure

_mcp-base/
├── server.ts            # Main MCP server implementation
├── client.ts            # Generic MCP client
├── utils/               # Utility services
│   ├── ollama_embedding.ts    # Embedding generation with Ollama
│   └── ollama_text_generation.ts  # Text generation with Ollama
├── tools/               # Tool implementations
│   └── sample-tool.ts   # Example tool template
├── resources/           # Resource implementations
│   └── sample-resource.ts  # Example resource template
├── prompts/             # Prompt implementations
│   └── sample-prompt.ts # Example prompt template
└── README.md            # This documentation

🚀 Getting Started

Prerequisites

- Node.js and npm/pnpm
- Ollama for local embedding and text generation
- Supabase account for vector storage

Environment Setup

Create a .env file with the following variables:

PORT=3000
SUPABASE_URL=https://your-project.supabase.co
SUPABASE_SERVICE_KEY=your-service-key
OLLAMA_URL=http://localhost:11434
OLLAMA_EMBED_MODEL=nomic-embed-text
OLLAMA_LLM_MODEL=llama3
SERVER_MODE=http  # 'http' or 'stdio'

Server Initialization

1. Import the required modules
2. Register your resources, tools, and prompts
3. Start the server

// Import base server and utilities
import server from "./server";
import { registerSampleResources } from "./resources/sample-resource";
import { registerSampleTool } from "./tools/sample-tool";
import { registerSamplePrompts } from "./prompts/sample-prompt";
// Initialize database if needed

async function initializeDatabase() {

  // Your database initialization logic

}
// Register your components

registerSampleResources(server, supabase);

registerSampleTool(server, textGenerator, embeddings, supabase);

registerSamplePrompts(server, supabase);
// Start the server

startServer();

Client Usage

import MCPClient from "./client";
// Create a client instance

const client = new MCPClient({

  serverUrl: "http://localhost:3000",

});
// Example: Call a tool

async function callSampleTool() {

  const result = await client.callTool("sample-tool", {

    query: "example query",

    maxResults: 5,

  });

  console.log(result);

}
// Example: Read a resource

async function readResource() {

  const items = await client.readResource("items://all");

  console.log(items);

}
// Example: Get a prompt

async function getPrompt() {

  const prompt = await client.getPrompt("simple-prompt", {

    task: "Explain quantum computing",

  });

  console.log(prompt);

}
// Don't forget to disconnect when done

await client.disconnect();

📚 Extending the Framework

Creating a New Tool

1. Create a new file in the tools/ directory
2. Define your tool function and schema using Zod
3. Implement your tool logic
4. Register the tool in your server

Creating a New Resource

1. Create a new file in the resources/ directory
2. Define your resource endpoints and schemas
3. Implement your resource logic
4. Register the resource in your server

Creating a New Prompt

1. Create a new file in the prompts/ directory
2. Define your prompt schema and parameters
3. Implement your prompt template
4. Register the prompt in your server

📄 License