get_quality_metrics

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

With no annotations, the description carries the full burden of behavioral disclosure. It only says 'Read', which implies a safe, non-mutating operation, and 'durable' hints at persisted metrics, but it does not clarify filtering behavior, optionality of parameters, or how the metrics relate to models, domains, or adapters. This is sparse for a tool with no annotation safety signals.

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?

The description is a single concise sentence with no redundancy or extra filler. However, the qualifier 'durable' is not explained and may create ambiguity, so it does not fully earn its place.

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?

Even with an output schema present, the description omits important contextual information: what quality metrics are tracked, what domain and adapter mean, whether combinations of parameters are valid, and what 'durable' implies about freshness or persistence. A read tool with three optional parameters and zero schema descriptions needs more context than this.

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%, so the description must compensate for the three parameters, but it does not. Only 'model' is hinted at by 'model quality metrics'; 'domain' and 'adapter' are left entirely unexplained. The parameter names and regex patterns do not provide sufficient semantic meaning for correct invocation.

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 ('Read') and a specific resource ('durable model quality metrics'), clearly distinguishing this from sibling tools that handle deliberations, decisions, and model sessions. No sibling appears to target quality metrics, so an agent can route to this tool without confusion.

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 intended use is implied: call this tool when you want model quality metrics. However, there is no explicit when-to-use guidance, no discussion of when not to use it, and no mention of alternatives such as query_decisions or list_models. The guidance is minimal.

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