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akb_search

Find relevant documents by combining semantic and keyword search, then access full content via returned URIs.

Instructions

Search documents with hybrid retrieval — dense vector (semantic) fused with BM25 sparse (keyword) via Reciprocal Rank Fusion. Handles both natural-language questions and short keyword queries well. For exact string / regex matches (code, URLs, version numbers) prefer akb_grep. Returns each hit's uri; use akb_drill_down or akb_get with that URI for full content. Response reports returned (in results) and total_matches (size of the deduped prefetch pool — NOT a corpus-wide hit count; vector ANN is top-K only). When truncated=true the prefetch pool was capped, meaning the corpus may hold more hits than reported — switch to akb_grep with count_only=true for an exact literal-substring count, or refine the query. When degraded=true the retrieval index hit a transient failure (vector-store outage or a degraded leg), so results may be incomplete or empty — this is NOT a genuine zero-match; degradation_reason names the cause. Retry shortly, or fall back to akb_grep for a literal search.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tagsNoFilter by tags
typeNoFilter by document type (any string). Common values: note, report, decision, spec, plan, session, task, reference, skill.
limitNo
queryYesNatural language search query
vaultNoLimit search to a specific vault
collectionNoLimit search to a specific collection
source_urisNoRestrict the search to a specific set of already-known resources by their canonical akb:// URIs (e.g. from a previous akb_search / akb_browse). Hybrid retrieval (dense + BM25 + ranking) runs only inside this set, intersected with the other filters and your access. Omit for the normal whole-vault search.
include_archivedNoInclude archived documents. Default false — `status: archived` docs are hidden from search.
Behavior5/5

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

No annotations, so description carries full burden. It discloses hybrid retrieval internals, the meaning of total_matches (not corpus-wide, top-K only), truncated=true behavior, degraded=true behavior, and degradation_reason. This goes far beyond basic safety and is exceptionally transparent.

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 longer than average but every sentence serves a purpose: core method, alternative, follow-up navigation, response semantics, truncated handling, degraded handling. Logical flow from purpose to exceptions; 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 no output schema and 8 parameters, description explains response fields (`returned`, `total_matches`), edge cases (`truncated`, `degraded`), and follow-up actions. It fully equips an agent to use results safely and to understand failure modes.

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 covers 88% of parameters with descriptions. The description adds nuance to `query` (handles natural-language questions and short keyword queries) and clarifies that exact matches should go to grep, but it doesn't elaborate on filter parameters beyond schema. This is a moderate addition over the high baseline.

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?

Description opens with 'Search documents with hybrid retrieval — dense vector (semantic) fused with BM25 sparse (keyword) via Reciprocal Rank Fusion.' This clearly states the verb (search), resource (documents), and method. It distinguishes from akb_grep ('exact string / regex matches') and akb_browse via sibling context.

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 directs users to akb_grep for exact string/regex matches, and toward akb_drill_down/akb_get for full content. Also instructs fallback to akb_grep when truncated or degraded, providing clear when-to-use vs alternatives.

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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