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MCP-Smallest.ai

by VinayakTiwari1103
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TypeScript
Remote
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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

Untitled-2025-03-21-0340(6)

┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐ │ │ │ │ │ │ │ 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 │ └─────────────────┘

Related MCP server: MCP Toolkit

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

    • Handles client requests

    • Manages API communication

    • Provides standardized responses

    • Implements error handling

  2. Knowledge Base Tools

    • listKnowledgeBases: Lists all knowledge bases

    • createKnowledgeBase: Creates new knowledge bases

    • getKnowledgeBase: Retrieves specific knowledge base details

  3. Documentation Resource

    • Available at docs://smallest.ai

    • Provides usage instructions and examples

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

  1. Install dependencies:

bun install

  1. 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: {} });

  1. Create Knowledge Base

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

  1. 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 } );

  1. 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

Deploy Server
A
security – no known vulnerabilities
F
license - not found
A
quality - confirmed to work

How are these scores calculated?

Related Resources

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