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vault_learn

Captures non-obvious learnings into an Obsidian vault: auto-routes them to the right note, domain MOC, daily note, and knowledge index, and returns a diff.

Instructions

Records a learning in the vault. Call it whenever something non-obvious and reusable comes up during the session — an architecture decision, a pattern, a gotcha, a configuration trap — without asking first where to save it: the server decides on its own between appending to the existing note that already covers the subject and creating a new one (the bias is to create), and propagates on its own to the domain MOC and the daily note (and to the knowledge index when the domain is new), all in a single commit. Show the returned diff to the user.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tagsNoTags for the note's frontmatter.
linksNoRelated wiki-links, without the brackets.
tituloYesShort title for the learning; it becomes the filename.
dominioYesDomain under `02-wiki/`, e.g. nestjs, docker, patterns. A new domain requires confirm_novo_dominio.
insightYesThe learning itself, in markdown.
projetoNoName of the project under `03-projects/` this learning belongs to; it goes into the daily note's capture line.
contextoYesWhere and why this came up.
confirm_novo_dominioNoConfirms creating a domain that does not yet exist under `02-wiki/`.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.3.0

TDQS

A4.3/5.0
Behavior5/5

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

With no annotations present, the description carries the full burden of behavioral disclosure and delivers it: server-side routing between append and create, propagation to domain MOC, daily note, and knowledge index for new domains, atomicity ('all in a single commit'), and the returned diff to show the user. This reveals side effects and internal logic that neither the name nor the schema conveys.

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 core action is front-loaded ('Records a learning in the vault') followed by usage triggers, routing behavior, propagation, and return handling. Every clause earns its place and nothing is wasted, though the single run-on sentence with nested dashes and parentheticals is harder to parse than shorter sentences would be.

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?

For a tool with no annotations and no output schema, the description covers purpose, when to call, routing decision, multi-target side effects, atomicity, and the diff return — nearly everything an agent needs. The main gaps are not mentioning the new-domain confirmation flow behind confirm_novo_dominio and not describing failure or error behavior, which is minor against the overall complexity.

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 baseline of 3 applies. The description adds modest extra color by tying the domain parameter to propagation behavior (knowledge index for new domains), but it does not need to compensate for missing schema text and does not explain individual parameter syntax beyond it.

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 — 'Records a learning in the vault' — with concrete examples of what counts (architecture decision, pattern, gotcha, configuration trap). The auto-routing behavior ('the server decides on its own... the bias is to create') clearly distinguishes it from the write/edit siblings, so an agent can tell them apart.

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 explicit trigger conditions: 'Call it whenever something non-obvious and reusable comes up' plus concrete scenarios, and instructs the agent not to ask where to save. However, it never names alternatives (e.g., vault_write_note, vault_edit_note) or states when not to use this tool, so the exclusion side 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.