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Refresh taxonomy snapshots

refresh_taxonomy_snapshots

Build taxonomy snapshots from active facts and sessions with a dry-run preview to verify output before writing live documents.

Instructions

Builds source-linked Memory Library taxonomy snapshots from active facts and sessions. Defaults to a dry run so workers can inspect planned snapshots before anything is written. Only pass dry_run=false after the dry-run output is safe and the job needs live write proof.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dry_runNoWhen true, preview snapshot output without writing Library docs.
max_sourcesNoMaximum facts or sessions to scan.
max_snapshotsNoMaximum taxonomy snapshots to build.
max_sources_per_snapshotNoMaximum source pointers per snapshot.
Behavior3/5

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

With no annotations, the description carries the full burden. It discloses the dry run feature and that live write creates documents, but does not mention potential side effects (e.g., whether it modifies source data), idempotency, or authentication requirements. This is adequate but could be more comprehensive.

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?

Two sentences efficiently convey purpose and usage. The first sentence front-loads the core function, and the second adds critical dry run guidance. No redundant information, every sentence earns its place.

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

Completeness3/5

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

While the description covers purpose and dry run, it lacks details about the output format (e.g., what a snapshot preview looks like in dry run, what 'source-linked' means) and how parameters like max_sources affect behavior. For a tool with no output schema, this leaves some ambiguity for the agent.

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?

All 4 parameters are described in the schema (100% coverage). The description adds semantic value by explaining the purpose of the dry_run parameter and when to set it false, which goes beyond the schema's 'preview snapshot output' phrasing. No extra semantics for other parameters, but the key parameter is enriched.

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 builds source-linked Memory Library taxonomy snapshots from active facts and sessions, using specific verbs and resources. It also distinguishes itself from the large set of unrelated sibling tools by describing its unique function.

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?

The description explicitly explains when to use dry run (default) and when to set dry_run=false, providing a clear workflow: inspect planned snapshots first, then write live if safe. This guides the agent on proper usage without needing to reference 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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