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

Import the required modules
Register your resources, tools, and prompts
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

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

Creating a New Resource

Create a new file in the resources/ directory
Define your resource endpoints and schemas
Implement your resource logic
Register the resource in your server

Creating a New Prompt

Create a new file in the prompts/ directory
Define your prompt schema and parameters
Implement your prompt template
Register the prompt in your server

📄 License

MIT

MCP Base

Integrations