Api metrology metric definitions list

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false, so safety is covered structurally. The description adds nothing an agent can use — 'serializer context' and 'optimized data' are backend jargon that do not tell the caller anything about result shape, pagination, or filtering behavior.

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 short, but the single sentence is spent on irrelevant implementation detail rather than front-loading what the tool does. Brevity here reflects under-specification, not economy.

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 13-parameter list tool with zero parameter documentation, no output schema, and a description that explains none of it is not callable with confidence. Nothing tells the agent what a returned metric definition looks like or how pagination via limit/offset/max_pages behaves.

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?

13 parameters with 0% schema description coverage and no explanations anywhere; the schema itself has empty anyOf items with no types or descriptions. The description does not name a single filter (id, unit, folder, search, ordering, max_pages, etc.), so an agent must guess the meaning of every parameter.

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 is an internal implementation note ('Override the list method to inject optimized data into the serializer context') rather than a statement of what the tool does for a caller. Only the tool name hints that it lists metric definitions; the description never says it returns a collection of metrology metric definitions or what those are.

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 when-to-use guidance, no mention of alternatives among the many metrology siblings (metric_definitions_retrieve, metric_instances_list, etc.), and no preconditions. The sentence is about Django internals, not about choosing this tool.

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