MCP-Smallest.ai

A Model Context Protocol (MCP) server implementation for Smallest.ai API integration. This project provides a standardized interface for interacting with Smallest.ai's knowledge base management system.

Architecture

System Overview

┌─────────────────┐     ┌─────────────────┐     ┌─────────────────┐
│                 │     │                 │     │                 │
│  Client App     │◄────┤   MCP Server    │◄────┤  Smallest.ai    │
│                 │     │                 │     │    API          │
└─────────────────┘     └─────────────────┘     └─────────────────┘

Component Details

1. Client Application Layer

• Implements MCP client protocol
• Handles request formatting
• Manages response parsing
• Provides error handling

2. MCP Server Layer

• Protocol Handler
  • Manages MCP protocol communication
  • Handles client connections
  • Routes requests to appropriate tools

• Tool Implementation
  • Knowledge base management tools
  • Parameter validation
  • Response formatting
  • Error handling

• API Integration
  • Smallest.ai API communication
  • Authentication management
  • Request/response handling

3. Smallest.ai API Layer

• Knowledge base management
• Data storage and retrieval
• Authentication and authorization

Data Flow

1. Client Request
   └─► MCP Protocol Validation
       └─► Tool Parameter Validation
           └─► API Request Formation
               └─► Smallest.ai API Call
                   └─► Response Processing
                       └─► Client Response

Security Architecture

┌─────────────────┐
│  Client Auth    │
└────────┬────────┘
         │
┌────────▼────────┐
│  MCP Validation │
└────────┬────────┘
         │
┌────────▼────────┐
│  API Auth       │
└────────┬────────┘
         │
┌────────▼────────┐
│  Smallest.ai    │
└─────────────────┘

Overview

This project implements an MCP server that acts as a middleware between clients and the Smallest.ai API. It provides a standardized way to interact with Smallest.ai's knowledge base management features through the Model Context Protocol.

Architecture

[Client Application]  [MCP Server]  [Smallest.ai API]

Components

1. MCP Server

2. Knowledge Base Tools

3. Documentation Resource

Prerequisites

• Node.js 18+ or Bun runtime
• Smallest.ai API key
• TypeScript knowledge

Installation

1. Clone the repository:

git clone https://github.com/yourusername/MCP-smallest.ai.git
cd MCP-smallest.ai

2. Install dependencies:

bun install

3. Create a `.env` file in the root directory:

SMALLEST_AI_API_KEY=your_api_key_here

Configuration

Create a `config.ts` file with your Smallest.ai API configuration:

export const config = {
    API_KEY: process.env.SMALLEST_AI_API_KEY,
    BASE_URL: 'https://atoms-api.smallest.ai/api/v1'
};

Usage

Starting the Server

bun run index.ts

Testing the Server

bun run test-client.ts

Available Tools

1. List Knowledge Bases

await client.callTool({
  name: "listKnowledgeBases",
  arguments: {}
});

2. Create Knowledge Base

await client.callTool({
  name: "createKnowledgeBase",
  arguments: {
    name: "My Knowledge Base",
    description: "Description of the knowledge base"
  }
});

3. Get Knowledge Base

await client.callTool({
  name: "getKnowledgeBase",
  arguments: {
    id: "knowledge_base_id"
  }
});

Response Format

All responses follow this structure:

{
  content: [{
    type: "text",
    text: JSON.stringify(data, null, 2)
  }]
}

Error Handling

The server implements comprehensive error handling:

• HTTP errors
• API errors
• Parameter validation errors
• Type-safe error responses

Development

Project Structure

MCP-smallest.ai/
├── index.ts           # MCP server implementation
├── test-client.ts     # Test client implementation
├── config.ts          # Configuration file
├── package.json       # Project dependencies
├── tsconfig.json      # TypeScript configuration
└── README.md          # This file

Adding New Tools

1. Define the tool in `index.ts`:

server.tool(
  "toolName",
  {
    param1: z.string(),
    param2: z.number()
  },
  async (args) => {
    // Implementation
  }
);

2. Update documentation in the resource:

server.resource(
  "documentation",
  "docs://smallest.ai",
  async (uri) => ({
    contents: [{
      uri: uri.href,
      text: `Updated documentation...`
    }]
  })
);

Security

• API keys are stored in environment variables
• All requests are authenticated
• Parameter validation is implemented
• Error messages are sanitized

Contributing

1. Fork the repository

2. Create your feature branch (`git checkout -b feature/amazing-feature`)

3. Commit your changes (`git commit -m 'Add some amazing feature'`)

4. Push to the branch (`git push origin feature/amazing-feature`)

5. Open a Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• Model Context Protocol
• Smallest.ai API
• Bun Runtime