auto_annotate_status

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

With annotations already declaring readOnlyHint=true and destructiveHint=false, the description goes well beyond them by disclosing the return semantics: both-null means never run, activeJob carries in-flight progress, lastRun carries failed/stopped booleans plus results or an error. This is exactly the behavioral context an agent needs since no output schema exists.

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 sentences, front-loaded with the purpose and addressing, followed by return semantics. Every clause carries information, though the enumeration of null cases is slightly dense.

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?

For a single-parameter, no-output-schema read tool, the description fully covers both how to address the dataset and what the return values mean, including edge cases. Nothing an agent needs to interpret the response is missing.

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?

Only one parameter with 100% schema description coverage, so the baseline is 4. The description restates the slug / owner-slug / ul:// URI forms already documented in the schema, adding no new syntax beyond it.

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 (Get) and resource (auto-annotation run's status) with the addressing forms for the dataset. It is clearly distinguishable from siblings auto_annotate_start/auto_annotate_stop, which mutate run state, while this only reads it.

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?

The description implies polling status after starting a run and gives the accepted dataset reference forms, but it never explicitly says when to call this versus auto_annotate_start/stop or when the result is meaningful. Usage is left to inference.

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