What can you do with this server?

The MCP-Smallest.ai server acts as middleware for interacting with Smallest.ai's knowledge base management system through the Model Context Protocol. It provides: * List Knowledge Bases: Retrieve a list of all available knowledge bases * Create Knowledge Base: Create a new knowledge base by providing a name and description * Get Knowledge Base: Retrieve details of a specific knowledge base by its ID * Standardized Interface: Ensures consistent communication with the Smallest.ai API * Validation and Error Handling: Manages parameter validation and error responses * MCP Protocol Support: Handles protocol communication and routes client requests Documentation is available via docs://smallest.ai

Which integrations are available for this server?

Supports running the MCP server on the Bun runtime, providing an alternative execution environment to Node.js for the server implementation. Utilizes environment variables for configuration management, specifically for storing the Smallest.ai API key securely. Hosts project repository and provides version control, allowing for collaborative development and contribution to the MCP server. Integrates with Smallest.ai's knowledge base management system, providing tools for listing, creating, and retrieving knowledge bases through the Smallest.ai API. Supports running the MCP server on Node.js 18+, providing the required runtime environment for server execution. Uses TypeScript for implementation, providing type-safe development of the MCP server and its integration with Smallest.ai.

How do I use MCP-Smallest.ai?

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-Smallest.ai list all my knowledge bases" 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.

de en es ja ko ru zh

MCP-Smallest.ai

by VinayakTiwari1103

Overview Schema Related Servers Score Discussions

TypeScript

Remote

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: Rememberizer MCP Server

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

MCP Server
- Handles client requests
- Manages API communication
- Provides standardized responses
- Implements error handling
Knowledge Base Tools
- listKnowledgeBases: Lists all knowledge bases
- createKnowledgeBase: Creates new knowledge bases
- getKnowledgeBase: Retrieves specific knowledge base details
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

Clone the repository:

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

Install dependencies:

bun install

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

List Knowledge Bases

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

Create Knowledge Base

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

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

Define the tool in index.ts:

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

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

Fork the repository
Create your feature branch (git checkout -b feature/amazing-feature)
Commit your changes (git commit -m 'Add some amazing feature')
Push to the branch (git push origin feature/amazing-feature)
Open a Pull Request

License

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

Acknowledgments

Available Tools

3 tools

createKnowledgeBaseD

ParametersJSON Schema

Name	Required	Description	Default
`description`	Yes
`name`	Yes

TDQS

D1/5.0

Behavior1/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Tool has no description.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness1/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Tool has no description.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness1/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Tool has no description.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters1/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Tool has no description.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose1/5

Does the description clearly state what the tool does and how it differs from similar tools?

Tool has no description.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines1/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Tool has no description.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

getKnowledgeBaseD

ParametersJSON Schema

Name	Required	Description	Default
`id`	Yes

TDQS

D1/5.0

Behavior1/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Tool has no description.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness1/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Tool has no description.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness1/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Tool has no description.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters1/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Tool has no description.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose1/5

Does the description clearly state what the tool does and how it differs from similar tools?

Tool has no description.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines1/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Tool has no description.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

listKnowledgeBasesD

ParametersJSON Schema

Name	Required	Description	Default
No parameters

TDQS

D1/5.0

Behavior1/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Tool has no description.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness1/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Tool has no description.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness1/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Tool has no description.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters1/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Tool has no description.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose1/5

Does the description clearly state what the tool does and how it differs from similar tools?

Tool has no description.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines1/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Tool has no description.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

TDQS

D1.9/5.0

Disambiguation5/5

Each tool has a clearly distinct purpose: create, get, and list operations on knowledge bases. There is no overlap in functionality, and the action verbs (create, get, list) are unambiguous and standard for CRUD operations.

Naming Consistency5/5

All tool names follow a consistent camelCase pattern with a verb-noun structure (createKnowledgeBase, getKnowledgeBase, listKnowledgeBases). The naming is predictable and uniform across all three tools.

Tool Count3/5

With only 3 tools, the set feels thin for a knowledge base management server, as it lacks update and delete operations. However, it covers basic create, retrieve, and list functions, which is minimal but functional for a small scope.

Completeness3/5

The tools provide create, get, and list operations, but there are notable gaps such as update and delete for knowledge bases. This limits full lifecycle management, though core retrieval and creation are covered.

Maintenance

ActivityInactive

ResponsivenessSyncing

Resources

GitHub Repository

Need Help?

Related Servers

Unclaimed servers have limited discoverability.

Looking for Admin?

If you are the server author, to access and configure the admin panel.

Related MCP Connectors

mcp COAuth
com.wix
A Model Context Protocol server for Wix AI tools
Developer Tools Content Management Systems App Automation
216
hithereiamaliff-mcp-nextcloudOAuth
ai.smithery
A comprehensive Model Context Protocol (MCP) server that enables AI assistants to interact with yo…
Cloud Storage File Systems Workplace & Productivity
237
Apideck MCP A
dev.apideck.mcp
Model Context Protocol server for the Apideck Unified API. Connect any MCP-compatible agent framework to 100+ accounting systems, HRIS platforms, file storage providers, and more through one integration. More information https://www.apideck.com/mcp-server
Developer Tools
42
docbase
io.github.krayinc
DocBase MCP server for AI agents
Documentation Access Search Developer Tools
2

Related MCP Servers

SourceSync.ai MCP Server
Knowledge & Memory Search RAG Systems
scmdr
A
license
B
quality
D
maintenance
A Model Context Protocol server that enables AI models to interact with SourceSync.ai's knowledge management platform for managing documents, ingesting content from various sources, and performing semantic searches.
Updated 2 years ago (2025-03-03 14:08 UTC)
25
17
1
ISC
Rememberizer MCP Server
Knowledge & Memory RAG Systems Databases
skydeckai
A
license
Not graded
quality
B
maintenance
A Model Context Protocol server enabling LLMs to search, retrieve, and manage documents through Rememberizer's knowledge management API.
Updated 5 months ago (2026-04-17 15:59 UTC)
35
Apache 2.0
MCP Conversation Server
RAG Systems Agent Orchestration Knowledge & Memory
bsmi021
A
license
Not graded
quality
D
maintenance
A Model Context Protocol server implementation that provides a standardized interface for applications to interact with OpenRouter's language models through a unified conversation management system.
Updated 2 years ago (2025-02-17 21:47 UTC)
18
2
MIT
Agent Knowledge MCP
Knowledge & Memory File Systems Search
itshare4u
A
license
A
quality
D
maintenance
A comprehensive Model Context Protocol server that integrates Elasticsearch search with file operations, document validation, and version control to transform AI assistants into powerful knowledge management systems.
Updated a year ago (2025-08-06 06:43 UTC)
27
27
MIT

Latest Blog Posts

Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)
By Om-Shree-0709 on July 25, 2026.
mcp
Agent Identity
OAuth 2.1
Your AI Chatbot Just Exposed Your CEO's Salary to an Intern
By Om-Shree-0709 on July 2, 2026.
Agent Identity
MCP Security
OAuth Delegation
Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)
By Om-Shree-0709 on June 30, 2026.
Agentic Ai
Prompt Injection
WebAssembly

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/VinayakTiwari1103/MCP-smallest-ai'

If you have feedback or need assistance with the MCP directory API, please join our Discord server