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knowledge_recall

Search a knowledge store using LIKE matching on title, body, and domain, or fetch a single page exactly by slug. Choose between full detail with citations or compact index entries, excluding archived pages.

Instructions

Search the knowledge store with LIKE matching over title, body, and domain, or fetch one page exactly by slug. Never surfaces archived pages. Two detail tiers: "full" returns whole entity pages (the synthesis unit) and stamps last_accessed/hit_count; "index" returns compact slug/domain/snippet entries without stamping. Defaults: full when a query is given, index when browsing without one. Full output is size-guarded — overflow results degrade to index entries; recall by slug to read them. Prefer slug over query when you know the page — token matching can hit cross-references in other pages' bodies.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
slugNoExact-slug lookup — returns that single page in full detail and stamps access. Takes precedence over query/domain/limit.
limitNoMaximum results to return (default: 10)
queryNoSearch terms — matched against title, body, and domain. Omit to browse (returns an index of non-archived pages up to limit).
detailNoOutput tier override. "index": compact listing, no body, no access stamping. "full": whole pages with citations. Default: full with a query, index without.
domainNoFilter by domain prefix, inclusive of the exact domain (e.g. "music/gear" matches "music/gear" and "music/gear/elektron")
sort_by_verifiedNoStale-first ordering for the verification engine: verified_at ASC with never-verified pages first. Index entries gain a "verified:" stamp so the SLA filter can run from the listing alone.
Behavior5/5

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

With no annotations, the description fully discloses behavior: archived pages are never surfaced, full detail stamps access/hit counts, output size-guarding causes overflow to index entries, and recall by slug to read full pages. Sort_by_verified behavior is also described.

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 description is a single dense paragraph but is front-loaded with the main purpose. Every sentence adds value, though it could be more structured (e.g., separate sentences for each tier). Still efficient and clear.

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 6 parameters, no output schema, and no annotations, the description covers all essential behavioral details, edge cases (size-guarding, defaults, slug precedence), and parameter interactions. It is complete for an agent to use correctly.

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

Parameters5/5

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

Schema coverage is 100% with descriptions for all 6 parameters. The description adds meaning beyond schema by explaining interactions (slug precedence, query vs browse, detail defaults, size-guarding logic, sort_by_verified timestamp behavior). This compensates fully.

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 searches the knowledge store using LIKE matching and slug lookup. It distinguishes between query-based search and exact-slug fetch, and explains the two detail tiers, making the purpose specific and differentiating from siblings like 'recall' or 'find_similar'.

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?

Provides explicit guidance on when to use slug over query ('Prefer slug over query when you know the page'), explains the default detail tier based on query presence, and describes the size-guarding behavior. This helps the agent choose correctly.

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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