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

Annotations already mark the operation as non-read-only, non-idempotent, and non-destructive. The description adds that execution is asynchronous, queued in the background, and returns only an accepted status, providing useful behavioral expectations beyond the annotations.

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?

Three short sentences front-load the action and parameter, then describe the queued behavior, returned status, and progress-tracking alternative. Every sentence contributes useful information without repetition.

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?

Given one required parameter, an output schema, and boolean annotations, the description covers the action, async behavior, returned status, and tracking path. It could mention how to obtain a valid data_source_id, but that is reasonably inferable from sibling list_data_sources.

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?

With 0% schema description coverage, the description compensates only partially by saying 'by its ID,' which confirms data_source_id is the identifier but adds no deeper meaning beyond the parameter name and schema type. The single simple parameter limits the impact of this gap.

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?

The description uses a specific verb and resource ('Trigger ... a data source run'), and adds that it is asynchronous and queued as a background job. This makes it clearly distinct from siblings like read_data_source_runs or list_data_sources.

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 names read_data_source_runs as the tool to track progress, which gives clear context for the follow-up workflow. It does not enumerate exclusion cases, but for a simple trigger operation the intended usage is evident.

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