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propose_graph_change

Propose adding or removing note graph connections, validate the request, preview changes in an image, and require user approval before applying anything.

Instructions

Propone cambiar conexiones del grafo (el related de las notas). NO cambia nada: valida el pedido, arma una imagen con los cambios marcados y el usuario la aprueba o la rechaza con un click. changes: [{"type": "add_link" | "remove_link", "source": "", "target": "", "label"?: "usa"}] (nombre, ruta o nombre de archivo de cada nota; hasta 10 cambios). reason: una línea con el porqué. Usala en vez de set_frontmatter(related) cuando el usuario pida conectar o desconectar notas.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
reasonNo
changesYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.1.3

TDQS

A4.9/5.0
Behavior5/5

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

With no annotations provided, the description carries the full burden and does so: it discloses that nothing is changed, that the request is validated, that an image with marked changes is produced, and that the user approves or rejects with a click. This non-mutating preview/approval flow is exactly the behavioral context an agent cannot get from structured fields.

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-loads the core purpose and the non-mutating guarantee, then the payload format, then routing guidance. Every sentence earns its place, though the inline format specification for `changes` makes it denser than a purely prose description.

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

Completeness5/5

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

For a two-parameter proposal tool with an output schema (the preview image) and no annotations, the description covers purpose, safety semantics, payload format, and sibling routing. Nothing an agent needs in order to call it correctly is missing.

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

Parameters5/5

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

Schema description coverage is 0%, so the description must compensate and does: it enumerates the `changes` item shape (type with allowed values 'add_link'|'remove_link', source, target, optional label), the accepted note identifier forms (nombre, ruta o nombre de archivo), a limit of 10 changes, and that `reason` is a one-line rationale. This exceeds the bare schema, which types `changes` only as an unconstrained array of objects.

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 and resource ('propone cambiar conexiones del grafo, el `related` de las notas') and immediately distinguishes itself from sibling write operations by declaring it is a proposal, not a mutation. An agent can tell this apart from set_frontmatter or write_file without opening a schema.

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

Usage Guidelines5/5

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

Explicitly routes the agent: 'Usala en vez de set_frontmatter(related) cuando el usuario pida conectar o desconectar notas.' It names the alternative sibling and the triggering user intent, leaving nothing to inference.

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