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MCP Server Template

by stevennevins
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TypeScript
Hybrid

Plantilla de servidor MCP

Prerrequisitos

Instalación de la CLI de LLM

Este proyecto requiere la instalación de la CLI de LLM. Puedes instalarla con Homebrew:

brew install llm

Después de la instalación, asegúrese de que el comando llm esté disponible en su RUTA ejecutando:

llm --version

Related MCP server: MCP Server Template

Empezando

Desarrollo

  1. Instalar dependencias:

    npm install

  2. Inicie el servidor de desarrollo con recarga en caliente:

    npm run dev

  3. Construir el proyecto:

    npm run build

  4. Ejecutar pruebas:

    npm test

  5. Inicie el servidor de producción:

    npm start

Herramienta de arquitecto

La herramienta Arquitecto ( src/tools/architect.ts ) proporciona una interfaz para interactuar con la CLI de LLM y obtener retroalimentación sobre el diseño arquitectónico. Mantiene el contexto de la conversación y gestiona la comunicación entre la aplicación y la CLI de LLM.

Características

  • Mantiene el contexto de la conversación a lo largo de múltiples interacciones.

  • Maneja la ejecución de comandos a través de la CLI de LLM

  • Proporciona gestión y registro de errores.

  • Admite tanto conversaciones nuevas como discusiones continuas.

Requisitos

  • La CLI de LLM debe estar instalada y disponible en su PATH (consulte la sección Requisitos previos)

  • Las variables de entorno deben configurarse correctamente para la CLI de LLM

Pruebas

La plantilla incluye un TestClient integrado para pruebas locales y el MCP Inspector para depuración visual.

Uso de TestClient

TestClient proporciona una forma sencilla de probar sus herramientas:

import { TestClient } from "./utils/TestClient";

describe("YourTool", () => {
  const client = new TestClient();

  it("should process data correctly", async () => {
    await client.assertToolCall(
      "your-tool-name",
      { input: "test" },
      (result) => {
        expect(result.toolResult.content).toBeDefined();
      }
    );
  });
});

Uso del inspector MCP

La plantilla incluye el Inspector MCP para la depuración visual de sus herramientas:

  1. Iniciar el inspector:

    npx @modelcontextprotocol/inspector node dist/index.js

  2. Abra la interfaz de usuario del inspector en http://localhost:5173

El inspector proporciona:

  • Interfaz visual para herramientas de prueba

  • Monitoreo de solicitudes y respuestas en tiempo real

  • Inspección de metadatos de la herramienta

  • Entorno de pruebas interactivo

Pruebas locales con cursor

Para probar su servidor MCP localmente con Cursor:

  1. Construya y vincule el paquete:

    npm run build
npm run link

  2. Verificar que el binario funciona:

    npx architect-test-mcp-tool

  3. Añade el servidor al Cursor:

    • Abrir la configuración del cursor

    • Vaya a la pestaña Características

    • Desplácese hacia abajo hasta la sección Servidores MCP

    • Haga clic en "Agregar servidor"

    • Seleccione el tipo "Comando"

    • Asígnele un nombre (por ejemplo, "Herramienta de ejemplo local")

    • Ingrese el comando: npx architect-test-mcp-tool

    • Haga clic en Confirmar

  4. Verifique que el servidor se inicie correctamente en Cursor verificando que la sección Servidores MCP muestre que su servidor está en ejecución.

Nota: Si realiza cambios en su código, recuerde reconstruirlo y volver a vincularlo:

npm run build
npm run link

Cuando hayas terminado de probar, puedes desvincular el paquete:

npm run unlink

Esto eliminará el enlace simbólico global creado durante el desarrollo.

Solución de problemas

Uso del inspector MCP

El Inspector MCP es una herramienta útil para depurar e inspeccionar su servidor MCP. Para usarlo:

  1. Primero, construye tu proyecto:

    npm run build

  2. Ejecute el inspector:

    npx @modelcontextprotocol/inspector node dist/index.js

El inspector proporciona una interfaz web que le permite:

  • Ver todas las herramientas disponibles y sus esquemas

  • La herramienta de prueba llama de forma interactiva

  • Inspeccionar las cargas útiles de solicitud/respuesta

  • Depurar problemas de comunicación entre el servidor y los clientes

Available Tools

1 tool
architectC

MCP server for the LLM Architect tool. Exposes resource "/llm-architect/chat" accepting POST requests with a prompt and optional conversationId, and interacts with the llm chat CLI to provide architectural design feedback while maintaining conversation context.

ParametersJSON Schema
NameRequiredDescriptionDefault
conversationIdNoOptional conversation ID for context
inputYesInput prompt to process

TDQS

C2.9/5.0
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions that the tool 'interacts with the llm chat CLI to provide architectural design feedback while maintaining conversation context,' which implies it's a read/write operation with state persistence. However, it lacks details on authentication needs, rate limits, error handling, or what specific 'architectural design feedback' entails. For a tool with no annotation coverage, this leaves significant gaps in understanding its behavior.

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

Conciseness4/5

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

The description is appropriately sized and front-loaded, starting with the tool's role as an MCP server and its key functionality. It uses two sentences efficiently: the first covers the resource and parameters, and the second explains the interaction with the CLI and purpose. There's minimal waste, though it could be slightly more concise by integrating the two ideas more tightly.

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

Completeness3/5

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

Given the complexity (a chat-based tool with conversation context), no annotations, and no output schema, the description is moderately complete. It explains the tool's purpose and parameters but lacks details on behavioral traits like response format, error cases, or specific feedback mechanisms. For a tool without structured output information, it should do more to compensate, but it provides a basic viable understanding.

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

Parameters3/5

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

The description adds some semantic context beyond the input schema. It mentions that the tool accepts 'a prompt and optional conversationId,' which aligns with the schema's 'input' (required) and 'conversationId' (optional) parameters. However, with 100% schema description coverage (both parameters have descriptions in the schema), the description doesn't provide additional meaning or usage examples. This meets the baseline of 3 for high schema coverage.

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

Purpose4/5

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

The description clearly states the tool's purpose: it's an MCP server that exposes a resource for POST requests to interact with an LLM chat CLI for architectural design feedback. It specifies the verb ('interacts with'), resource ('/llm-architect/chat'), and context ('maintaining conversation context'). However, since there are no sibling tools mentioned, it doesn't need to distinguish from alternatives, so it falls just short of a perfect score.

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

Usage Guidelines2/5

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

The description provides minimal usage guidance. It mentions that the tool accepts POST requests with a prompt and optional conversationId, but it doesn't explain when to use this tool versus other potential tools (though none are listed as siblings), nor does it provide context about when this specific architectural feedback tool is appropriate versus general chat tools. No exclusions or alternatives are mentioned.

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. 1 tool updatev1.0.0
    • First observedarchitect
Deploy Server
Shareable, User-Owned Context BackendPeople, applications, and agents read, write, and coordinate together in one context lake. Reach it over MCP, a CLI, a Python SDK, or REST.fulcradynamics.comAd

TDQS

B3/5.0

Scored across 1 tool

Disambiguation5/5

With only one tool, there is no possibility of ambiguity or overlap between tools. The tool 'architect' has a clearly defined and distinct purpose, so an agent cannot misselect between non-existent alternatives.

Naming Consistency5/5

A single tool inherently exhibits perfect naming consistency, as there are no other tools to compare against. The name 'architect' follows a simple, readable pattern without any conflicting conventions.

Tool Count2/5

One tool is too few for a server named 'MCP Server Template', which implies a broader or more general-purpose scope. A template server should ideally offer multiple tools to demonstrate a range of capabilities, making this count inappropriate for the apparent purpose.

Completeness1/5

The server is severely incomplete for its implied domain as a template. It only provides a single chat interaction tool, lacking any CRUD operations, configuration tools, or other functionalities expected from a template that should showcase a comprehensive tool surface for agents to learn from.

Maintenance

ActivityInactive
ResponsivenessNo issues

Resources

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