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get_guide

Read-only

La guía pública de Secondbrain (secondbrain.media/guide): cómo funciona cada parte —el Diario, las tareas, los recaps de reuniones, el Atlas, el calendario, Teams, el bot de WhatsApp y Telegram, el clipper, este MCP—, los precios y las descargas.

Para preguntas sobre el PRODUCTO (qué es, cómo se usa, qué hace, cuánto
cuesta), no sobre los datos de la persona. Devuelve las secciones que mejor
contestan, con su texto, su enlace (url) y sus piezas visuales —capturas,
videos y diagramas de la guía—, más otras secciones cercanas. Con
`seccion` trae una sección exacta por su clave (pagina#seccion).

Input Schema

NameRequiredDescriptionDefault
idiomaNoIdioma de la conversación
seccionNoClave exacta de una sección (p. ej. "recaps#grabar"), de una respuesta anterior
preguntaNoLo que se quiere saber, con las palabras de la persona

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.1/5.0
Behavior4/5

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

Annotations already declare readOnly/openWorld/non-destructive, so the safety profile is covered. The description adds genuine behavioral context that annotations cannot: what comes back (matching sections with text, url and visual assets — screenshots, videos, diagrams) plus nearby sections, and the openWorld nature of a public web guide.

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?

Front-loaded with purpose, then the product-vs-data boundary, then return shape and the exact-section mode. The long content enumeration is somewhat verbose but plausibly earns its place by telling the agent what topics the guide can answer.

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

Completeness4/5

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

With no output schema, the description carries the burden of explaining returns and does so (sections, text, url, visual pieces, related sections). For a 3-param, fully-described retrieval tool this is close to complete, though it says nothing about coverage limits or fallback when no section matches.

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?

Schema description coverage is 100%, so the schema already documents idioma, seccion and pregunta. The description restates the seccion key format (pagina#seccion) and its origin ("de una respuesta anterior"), which is already in the schema, adding little beyond the baseline.

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

Purpose5/5

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

States a specific verb+resource ("Devuelve las secciones" of "la guía pública de Secondbrain") and enumerates what the guide covers (Diario, tareas, recaps, Atlas, calendario, Teams, bots, clipper, precios, descargas). It also draws a hard boundary against the many data-retrieval siblings by scoping itself to product questions, not personal data.

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

Usage Guidelines4/5

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

Gives an explicit when-to-use ("preguntas sobre el PRODUCTO: qué es, cómo se usa, qué hace, cuánto cuesta") and an explicit when-not ("no sobre los datos de la persona"). It never names the closest help-oriented sibling (how_to_in_app), so the alternative selection is left to inference.

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

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