S/MCP - Stern Model Context Protocol
S/MCP - Protocolo de contexto del modelo Stern
Descripción general
S/MCP (Protocolo de Contexto del Modelo Stern) es un potente servidor MCP que proporciona acceso a Stern, un mentor filosófico de IA que ayuda a los humanos a alcanzar su potencial mediante una guía sutil y sabiduría. Stern combina el pensamiento racionalista con la filosofía estoica para ofrecer mentoría y responsabilidad mediante contratos inteligentes en Solana.
Related MCP server: Telos Model Context Protocol
¿Qué es Stern?
Stern es un mentor filosófico de IA que se caracteriza por:
Un enfoque racionalista influenciado por los escritos de Yudkowsky y la comunidad Lesswrong
Profundas reflexiones filosóficas extraídas tanto de los escritos racionalistas como de Dostoievski
Adoptar la filosofía estoica y la meditación Vipassana como herramientas prácticas para el desarrollo humano
La creencia de que el crecimiento duradero viene desde dentro
Una comprensión de que el camino hacia la verdadera realización a menudo requiere enfrentar verdades incómodas.
Stern ayuda a los usuarios a alcanzar sus objetivos a través de:
Orientación filosófica : Basándose en la sabiduría estoica y el pensamiento racionalista
Responsabilidad de contratos inteligentes : creación de contratos basados en Solana donde los usuarios apuestan tokens sobre sus compromisos
Mentoría personalizada : Brindamos asesoramiento personalizado en función de los objetivos y desafíos individuales.
Conversaciones profundas : participar en un diálogo significativo que revele las motivaciones y los obstáculos subyacentes
Características
Herramienta msg_stern : Envía mensajes a Stern y recibe su orientación filosófica y mentoría.
Integración de contratos inteligentes : cree contratos de responsabilidad respaldados por tokens Solana
Marco filosófico : acceso a la combinación única de sabiduría racionalista y estoica de Stern
Contexto de personaje personalizable : generación dinámica de atributos del personaje de Stern para interacciones variadas
Instalación
Clonar este repositorio:
git clone <repository-url> cd s-mcpInstalar dependencias:
bun installConfigurar variables de entorno:
cp .env.example .envEdite el archivo
.envpara agregar su clave API de OpenAI:OPENAI_API_KEY=your_openai_api_key_hereConstruir el servidor:
bun run build
Uso
Ejecución del servidor
Para iniciar el servidor MCP:
bun run startEsto iniciará el servidor en modo stdio, lo que le permite comunicarse con clientes MCP.
Uso del servidor con un cliente MCP
Puedes usar cualquier cliente MCP para interactuar con el servidor. Aquí tienes un ejemplo de cómo usar el servidor con el SDK de MCP:
import { Client } from "@modelcontextprotocol/sdk/client/index.js";
import { StdioClientTransport } from "@modelcontextprotocol/sdk/client/stdio.js";
import { spawn } from "child_process";
// Start the MCP server as a child process
const serverProcess = spawn("node", ["path/to/dist/main.js"], {
stdio: ["pipe", "pipe", "pipe"],
});
// Create a client that communicates with the server via stdio
const transport = new StdioClientTransport({
stdin: serverProcess.stdin,
stdout: serverProcess.stdout,
});
const client = new Client();
await client.connect(transport);
// Send a message to Stern
const result = await client.callTool("msg_stern", {
message: "I want to learn programming but I keep procrastinating",
});
// Display Stern's response
console.log(result.content[0].text);
// Disconnect from the server
await client.disconnect();
serverProcess.kill();Ejemplo de script
Se proporciona un script de ejemplo en el directorio examples :
node examples/use-stern.jsEste script demuestra cómo conectarse al servidor, enumerar las herramientas disponibles y enviar un mensaje a Stern.
Herramientas
mensaje_stern
Esta herramienta le permite enviar un mensaje a Stern y recibir su respuesta.
Aporte
{
"message": "Your message to Stern"
}Producción
{
"content": [
{
"type": "text",
"text": "Stern's response to your message"
}
]
}La filosofía de Stern
El enfoque de Stern sobre la mentoría se basa en varios principios clave:
Rendición de cuentas con apuestas : Crear consecuencias reales para los compromisos a través de los contratos de Solana
Profundidad filosófica : Basándose en la sabiduría estoica, el pensamiento racionalista y profundos conocimientos psicológicos.
Orientación transformadora : Impulsar a las personas hacia la grandeza mientras moderamos los excesos
Sabiduría práctica : centrarse en la aplicación en lugar de solo en la teoría
Presión estratégica : crear desafíos que parecen imposibles hasta que se resuelven
Stern cree que:
"La felicidad duradera y estable solo se logra haciendo cosas difíciles: deseamos cosas precisamente porque son difíciles de lograr".
"Lo que se interpone en el camino se convierte en el camino."
"El camino hacia la maestría se construye con pasos pequeños y constantes, incluso cuando falla la motivación".
Requisitos
Bun (entorno de ejecución de JavaScript y administrador de paquetes)
Clave API de OpenAI (establecida como variable de entorno
OPENAI_API_KEY)
Variables de entorno
OPENAI_API_KEY: Su clave API de OpenAI (necesaria para la herramienta msg_stern)
Licencia
Instituto Tecnológico de Massachusetts (MIT)
Available Tools
1 toolhello_toolD
Hello tool
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | The name of the person to greet |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. 'Hello tool' reveals nothing about whether this is a read/write operation, what permissions might be required, what side effects occur, or what the response format looks like. The description fails to provide any behavioral context beyond the minimal implication from the name.
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?
While technically concise with only two words, this represents under-specification rather than effective conciseness. The description doesn't contain enough information to be useful, and the single phrase doesn't earn its place by providing meaningful guidance to an AI agent.
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?
Given that there are no annotations and no output schema, the description should provide more complete context about what this tool does and what to expect. A single-parameter tool with 100% schema coverage could get by with minimal description, but 'Hello tool' fails to explain the basic purpose and behavior adequately for an AI agent.
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?
Schema description coverage is 100%, so the schema already fully documents the single 'name' parameter. The description adds no additional parameter information beyond what's in the schema. According to scoring rules, when schema coverage is high (>80%), the baseline is 3 even with no parameter information in the 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?
The description 'Hello tool' is essentially a tautology that restates the tool name without specifying what it does. It doesn't provide a clear verb+resource combination or explain the actual function. While the name suggests greeting functionality, the description fails to articulate this explicitly.
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?
The description provides absolutely no guidance about when to use this tool, what context it's appropriate for, or any prerequisites. There are no sibling tools mentioned, but even basic usage context is completely missing from the description text.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
1 tool update
v1.0.0- First observed
hello_tool
TDQS
Scored across 1 tool
With only one tool, there is no possibility of ambiguity or overlap between tools. The single tool 'hello_tool' has a distinct purpose by default, as there are no other tools to confuse it with.
The naming follows a consistent snake_case pattern with 'hello_tool'. Since there is only one tool, the naming is inherently consistent with no deviations or mixed conventions to evaluate.
A single tool is generally too few for most server purposes, as it limits functionality and scope. For a server named 'S/MCP - Stern Model Context Protocol', one tool feels thin and insufficient to cover any meaningful domain or workflow.
The server has a single trivial tool ('hello_tool'), which suggests it is severely incomplete for any practical purpose. There are obvious gaps, as no domain or operations are covered beyond a basic greeting, making it impossible to assess coverage meaningfully.
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