How do I use MCP Base?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@MCP Base help me create a new tool for analyzing customer feedback" 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.

Supported for environment configuration, allowing users to set up connection parameters for integrated services and server settings.

Provides the runtime environment for the MCP server, allowing it to execute JavaScript/TypeScript code and handle HTTP and stdio transports.

Provides services for generating embeddings and text with Ollama, allowing AI-powered applications to perform embedding generation and text generation operations locally.

Supported as a package manager for installing and managing dependencies of the MCP server implementation.

Built-in support for Supabase vector database, enabling storage and retrieval of vector embeddings for AI applications.

The MCP server is implemented in TypeScript, providing type safety and improved developer experience when extending the framework.

Used for schema validation in tools, resources, and prompts, enabling type-safe interactions with the MCP server's components.

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

Related MCP server: PAELLADOC

🛠️ 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