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

Annotations already establish the safe read-only, non-destructive, closed-world profile, so this is not the description's main job. The description nonetheless adds genuinely useful behavioral context: the numbers are AMT's own and must not be recalculated or invented, and row text/labels/file names are untrusted user data that may contain injected instructions.

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?

Two tightly packed sentences with no filler; the content list is front-loaded and the safety constraints follow. The enumerated list is slightly dense but every element maps to real return content.

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 annotations covering the safety profile and the parameter fully described in the schema, the description's job is mainly to explain what comes back — which it does, since there is no output schema. It omits only explicit routing to sibling tools, a minor gap for a simple read operation.

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 description coverage is 100% and the single model_id parameter is fully documented in the schema ('from list_models'), so the description carries no extra burden. The description adds no parameter detail beyond the schema, which is the expected baseline for full coverage.

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 the specific resource (the model's training data) and enumerates the returned content — file, rows, columns, category column, category counts, status — so an agent knows it is a summary/read tool rather than a mutation. It does not explicitly contrast itself with near-neighbors like model_report or columns, keeping it just short of a 5.

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?

There is no statement of when to call this versus the many sibling analysis tools (columns, data_quality, model_report). The only imperative guidance is about how to report results faithfully, not about tool selection, so usage must be inferred from the name alone.

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