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otolab
by otolab

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Search local Markdown docs with natural language queries and get relevant sections ranked by relevance.

Instructions

ドキュメントをVector検索します。関連するセクションが関連性順で返されます。

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
depthNo最大深度(0-3)。この深度まで検索します。0=文書全体のみ、1=章まで、2=節まで、3=項まで。省略時は全階層を検索
limitNo結果数制限(デフォルト: 10)
queryYes検索クエリ
projectNo関連プロジェクト名(未指定時はメインプロジェクト)
syncedOnlyNoインデックスがドキュメントと同期済みのセクションのみを検索対象にする(デフォルト: false、未同期のセクションも含めて検索)
excludePathsNo除外するドキュメントパス(前方一致)。例: ["docs/internal/", "temp/"]
includePathsNo包含するドキュメントパス(前方一致)。例: ["docs/", "README.md"]
previewLinesNoプレビュー行数(デフォルト: 5)
Behavior2/5

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

With no annotations, the description carries the full burden of behavioral disclosure. It discloses that results are returned in relevance order, which is a useful behavioral detail, but it does not mention any side effects, permissions, rate limits, or other operational characteristics. The bare description is insufficient for a tool with no annotation support.

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 a single concise sentence that front-loads the primary action ('Vector search documents') and states the output format. Every word earns its place, and there is no redundant or filler content.

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

Completeness2/5

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

While the schema richly describes parameters, there is no output schema, and the description only states that 'related sections are returned in order of relevance.' It lacks details about the structure of returned sections (e.g., whether they include paths, text, scores) or any additional behavioral context. For a tool with several parameters and no output schema, this is insufficient.

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 schema description coverage is 100%, with all 8 parameters documented in the input schema. The description adds no additional parameter information, but since the schema fully covers semantics, the baseline score of 3 is appropriate.

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 states that this tool performs vector search on documents and returns relevant sections in order of relevance. The verb 'search' and resource 'documents' are specific, and it distinguishes itself from siblings like get_document and get_outline by focusing on search rather than retrieval of a single document or outline.

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

Usage Guidelines3/5

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

The description implies usage for searching documents but does not explicitly state when to use this tool versus alternatives. No when-not-to-use or alternative tool references are provided. The context is clear enough for a simple search tool, but exclusions or comparisons with siblings are missing.

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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