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

Despite annotations only declaring destructive/not-read-only, the description adds substantial behavioral context: CPU mode reads nothing again and charges nothing, GPU mode is a fresh charged read on every call, and GPU runs as a two-step flow with a confirmation code like train. Cost and confirmation semantics are exactly what the agent needs.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The critical CPU-vs-GPU distinction is front-loaded and no sentence is wasted, though the prose is dense and run-on, requiring close reading. Slightly more structure would improve scanability.

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

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

With no output schema and a mutating, cost-bearing tool, the description covers cost, confirmation flow, mode selection, and a reporting directive ('never calculate, estimate or invent one'). It omits what happens to the suggestions afterward (e.g., review/accept via review_suggestions), a minor gap.

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

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

Schema coverage is 50%, and the description compensates well: use_gpu is tied to Business eligibility, charging, and row re-reads; categories are explained as becoming the model's categories; confirmation is tied to the two-step flow. model_id gains little beyond the schema hint, but overall meaning is well extended.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource with scope: 'Suggest a category for every unreviewed row of a cold start.' It is clearly distinguishable from siblings like start_fill, train, and classify, and it explains the two operating modes without ambiguity.

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

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

Gives conditional guidance on the two modes (categories supplied vs. not) and spells out when use_gpu applies (Business plan, re-reads rows, charged). It does not explicitly route the agent away from siblings such as start_fill or train, so it falls short of full when/when-not guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.