Implementación de un servidor del Protocolo de Contexto de Modelo (MCP) para la integración de la API de Smallest.ai. Este proyecto proporciona una interfaz estandarizada para interactuar con el sistema de gestión de la base de conocimientos de Smallest.ai.
┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐
│ │ │ │ │ │
│ Client App │◄────┤ MCP Server │◄────┤ Smallest.ai │
│ │ │ │ │ API │
└─────────────────┘ └─────────────────┘ └─────────────────┘Implementa el protocolo de cliente MCP
Maneja el formato de la solicitud
Gestiona el análisis de respuestas
Proporciona manejo de errores
Manejador de protocolo
Gestiona la comunicación del protocolo MCP
Maneja las conexiones del cliente
Envía solicitudes a las herramientas adecuadas
Implementación de herramientas
Herramientas de gestión de bases de conocimientos
Validación de parámetros
Formato de respuesta
Manejo de errores
Integración de API
Comunicación API de Smallest.ai
Gestión de autenticación
Manejo de solicitudes/respuestas
Gestión de la base de conocimientos
Almacenamiento y recuperación de datos
Autenticación y autorización
1. Client Request
└─► MCP Protocol Validation
└─► Tool Parameter Validation
└─► API Request Formation
└─► Smallest.ai API Call
└─► Response Processing
└─► Client Response┌─────────────────┐
│ Client Auth │
└────────┬────────┘
│
┌────────▼────────┐
│ MCP Validation │
└────────┬────────┘
│
┌────────▼────────┐
│ API Auth │
└────────┬────────┘
│
┌────────▼────────┐
│ Smallest.ai │
└─────────────────┘Related MCP server: Rememberizer MCP Server
Este proyecto implementa un servidor MCP que actúa como middleware entre los clientes y la API de Smallest.ai. Proporciona una forma estandarizada de interactuar con las funciones de gestión de la base de conocimiento de Smallest.ai mediante el Protocolo de Contexto de Modelo.
[Client Application] <---> [MCP Server] <---> [Smallest.ai API]Servidor MCP
Maneja las solicitudes de los clientes
Gestiona la comunicación API
Proporciona respuestas estandarizadas
Implementa el manejo de errores
Herramientas de la base de conocimientos
listKnowledgeBases : enumera todas las bases de conocimiento
createKnowledgeBase : Crea nuevas bases de conocimiento
getKnowledgeBase : recupera detalles específicos de la base de conocimientos
Recurso de documentación
Disponible en docs://smallest.ai
Proporciona instrucciones de uso y ejemplos.
Node.js 18+ o entorno de ejecución de Bun
Clave API de Smallest.ai
Conocimiento de TypeScript
Clonar el repositorio:
git clone https://github.com/yourusername/MCP-smallest.ai.git
cd MCP-smallest.aiInstalar dependencias:
bun installCree un archivo .env en el directorio raíz:
SMALLEST_AI_API_KEY=your_api_key_hereCree un archivo config.ts con la configuración de su API de Smallest.ai:
export const config = {
API_KEY: process.env.SMALLEST_AI_API_KEY,
BASE_URL: 'https://atoms-api.smallest.ai/api/v1'
};bun run index.tsbun run test-client.tsLista de bases de conocimiento
await client.callTool({
name: "listKnowledgeBases",
arguments: {}
});Crear una base de conocimientos
await client.callTool({
name: "createKnowledgeBase",
arguments: {
name: "My Knowledge Base",
description: "Description of the knowledge base"
}
});Obtener base de conocimientos
await client.callTool({
name: "getKnowledgeBase",
arguments: {
id: "knowledge_base_id"
}
});Todas las respuestas siguen esta estructura:
{
content: [{
type: "text",
text: JSON.stringify(data, null, 2)
}]
}El servidor implementa un manejo integral de errores:
Errores HTTP
Errores de API
Errores de validación de parámetros
Respuestas de error de tipo seguro
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 fileDefine la herramienta en index.ts :
server.tool(
"toolName",
{
param1: z.string(),
param2: z.number()
},
async (args) => {
// Implementation
}
);Actualizar la documentación en el recurso:
server.resource(
"documentation",
"docs://smallest.ai",
async (uri) => ({
contents: [{
uri: uri.href,
text: `Updated documentation...`
}]
})
);Las claves API se almacenan en variables de entorno
Todas las solicitudes están autenticadas
Se implementa la validación de parámetros
Los mensajes de error se desinfectan
Bifurcar el repositorio
Crea tu rama de funciones ( git checkout -b feature/amazing-feature )
Confirme sus cambios ( git commit -m 'Add some amazing feature' )
Empujar a la rama ( git push origin feature/amazing-feature )
Abrir una solicitud de extracción
Este proyecto está licenciado bajo la licencia MIT: consulte el archivo de LICENCIA para obtener más detalles.
| Name | Required | Description | Default |
|---|---|---|---|
| description | Yes | ||
| name | Yes |
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.
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.
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.
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.
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.
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.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes |
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.
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.
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.
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.
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.
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.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
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.
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.
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.
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.
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.
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.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
v1.0.0createKnowledgeBasegetKnowledgeBaselistKnowledgeBasesScored across 3 tools
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.
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.
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.
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.
A Model Context Protocol server for Wix AI tools
A comprehensive Model Context Protocol (MCP) server that enables AI assistants to interact with yo…
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
DocBase MCP server for AI agents
