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search_concepts

Find concepts in an OKF knowledge bundle by full-text, tags, or type. Combine filters for case-insensitive substring searches across titles, descriptions, IDs, and body content for precise results.

Instructions

Search concepts by full-text query, tags, and/or type.

Case-insensitive substring search across concept IDs, titles, descriptions, and body content. All provided filters are ANDed together.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pathNoRestrict the search to a subdirectory.
tagsNoReturn only concepts that carry ALL of these tags.
queryNoText to search for. Matched against concept ID, title, description, and body.
type_filterNoReturn only concepts of exactly this type (case-insensitive).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

No annotations are provided, so the description carries the behavioral transparency burden. It discloses key behaviors: case-insensitive substring search across specific fields, and AND-combination of all provided filters. This adds meaningful context beyond basic search semantics, though it doesn't cover pagination or edge cases.

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 two sentences, front-loaded with the primary action and followed by precise behavioral details. No filler or redundancy; every sentence earns its place.

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?

Given 4 optional parameters and an existing output schema, the description covers the essential search scope and filter combination logic. It doesn't mention pagination or ordering, but these are likely covered by the output schema or default behavior, not required for basic understanding.

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

Parameters4/5

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

Schema coverage is 100%, providing baseline 3. The description adds semantic value by explaining how parameters interact: all filters are ANDed together, and search is case-insensitive substring across multiple fields. This enriches understanding beyond individual parameter descriptions.

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 the tool's function: search concepts by full-text query, tags, and/or type. It uses a specific verb and resource, and distinguishes from siblings like get_concept (single concept retrieval) and list_concepts (listing without search filters) by indicating the search-based scope.

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?

Usage context is clear: use this tool when you need to find concepts matching a query, tags, or type. It doesn't explicitly name alternatives or exclusion cases, but the search-focused description implies a distinct role among siblings.

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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