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

Goes well beyond the annotations by disclosing that reading is metered like a fill (rows count toward a monthly quota, pictures toward pictures read), that reading is queued and may not start immediately, and that start requires explicit user confirmation via a returned code. It also instructs the agent to report AMT's numbers as given and never fabricate them, which is important behavioral guidance for a destructive/quota-consuming tool.

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?

Dense and front-loaded with the primary purpose, and every clause carries operational weight (charging, queuing, confirmation, number fidelity). However, it is a single run-on paragraph with some awkward phrasing ('with no other arguments, says where its cold start is') that slightly impedes fast parsing.

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 5 parameters, the description carries a lot: it describes what the first call returns (what it will use plus a confirmation code), the async queuing behavior, and quota charging. It stops short of describing the second call's return or error/edge cases, but the agent has enough to invoke it correctly.

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 100%, so the baseline is 3, but the description adds cross-call semantics the schema can't: columns and confirmation interlock across two invocations, and use_gpu's trade-off (GPU Dedicated AI Model can suggest categories itself vs. CPU AI Classifier) is tied to outcome. It also explains label_column's effect on which rows count as reviewed. Only marginal added detail per individual parameter, so not a 5.

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: for a model with unlabelled rows, it either reports cold-start status or begins reading rows so AMT can suggest categories. The two operating modes (status-only vs. reading with columns) are explicit. Sibling differentiation is partial — it references create_model's upload link and AMT's process but never names start_fill or suggest_labels as alternatives.

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 clear conditions for each mode: no other arguments to see cold-start status, columns to begin reading. The two-step confirmation flow (first call returns a code; only call again after explicit user agreement) tells the agent exactly how to proceed. No explicit when-not or named alternative tool, so not a 5.

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