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vault_search

Search the vault with lexical-semantic matching to find decisions, patterns, and gotchas, returning cited snippets for answering questions or recording new learnings.

Instructions

Lexical-semantic search over the vault (BM25 plus one wiki-link hop). Call it before answering any question about the user's decisions, patterns, gotchas or history, and before recording a new learning. Returns snippets already cited as path:line — repeat that citation in your answer. Notes under 01-raw/ are excluded unless include_raw.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tagsNoFilters by frontmatter tags. A note must carry ALL of them; case does not matter.
tipoNoFilter by the frontmatter `tipo`: wiki, moc, projeto, daily.
limitNoMaximum snippets returned (default 6).
queryYesSearch terms in natural language.
folderNoRestrict to a vault folder, e.g. `02-wiki/nestjs`.
statusNoFilters by the frontmatter `status`, e.g. `ativo`.
include_rawNoInclude `01-raw/` (unvetted capture), left out of results by default.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.3.0

TDQS

A4.3/5.0
Behavior4/5

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

With no annotations, the description carries the full burden, and it discloses the key behavioral traits: search quality, exclusion of 01-raw notes unless include_raw, and the path:line citation format. It does not need to discuss side effects because the tool name clearly indicates a read-only search operation.

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

Conciseness5/5

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

The description is compact and front-loaded: purpose first, then usage, then output behavior, then the raw-notes exception. Every sentence conveys a distinct, necessary piece of information with no filler.

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?

Given the 7-parameter schema, the absence of annotations, and the absence of an output schema, the description covers what the agent needs most: when to use the tool, how results are formatted, and the one non-obvious inclusion/exclusion rule. The remaining parameter details are fully handled by the schema.

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 all seven parameters are already documented in the input schema. The description only restates the include_raw behavior, which is already expressed in the schema, so it adds no meaningful semantic value beyond what the agent already has.

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?

The description clearly defines the tool as a vault-wide lexical-semantic search using BM25 plus one wiki-link hop, which is specific and immediately distinguishes it from sibling tools like vault_list and vault_backlinks. It states the verb, resource, and retrieval behavior without ambiguity.

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?

The description gives explicit instructions on when to call it: before answering questions about the user's decisions, patterns, gotchas, or history, and before recording new learnings. It does not explicitly state when not to use it or name an alternative tool, but the context is clear enough for correct routing.

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