Api metrology dashboard widgets list

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

Annotations already declare readOnly, idempotent, non-destructive and openWorld, so the safety profile is covered. The description adds only that data is 'optimized' for the serializer, which is a server-side performance note with no bearing on what the agent receives or how results are shaped.

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?

It is a single short sentence, so it is compact, but the content is internal developer jargon that does not front-load the tool's purpose or any actionable information for a caller.

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?

A 12-parameter tool with no output schema, no parameter documentation, and no annotation-independent behavioral detail. The description does nothing to close any of those gaps, leaving an agent unable to call it correctly beyond guessing at parameter names.

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 0% across 12 parameters (id, limit, folder, offset, search, ordering, dashboard, max_pages, chart_type, time_range, aggregation, metric_instance) and the description explains none of them. Filtering, pagination, and widget-configuration semantics are entirely undocumented.

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 says it 'overrides the list method' and 'injects optimized data into the serializer context' – both internal Django implementation details rather than a statement of what the tool returns. Only by combining the tool name (dashboard widgets list) can an agent infer it lists metrology dashboard widgets; the description itself never names the resource.

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?

No when-to-use, when-not-to-use, or alternative is given. With a dozen sibling metrology endpoints (retrieve, object_retrieve, cascade_info, aggregation, chart_type), the agent receives no signal about which to pick.

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