label_values

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

With no annotations, the description carries the full burden. It does disclose the read-only nature ('[READ]') and the truncation behavior via the returned envelope. However, it advises 'raise the limit' without an actual limit parameter in the schema, which is misleading. This prevents it from being highly transparent, as the agent cannot follow the guidance.

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 efficiently structured with an Args section and a return envelope explanation. It is front-loaded with the purpose and uses concise, informative sentences. The only minor issue is the unactionable 'raise the limit' advice, but overall it is well-organized and without fluff.

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 the lack of an output schema and annotations, the description covers the essentials: purpose, parameters, return format, and truncation handling. However, it references a 'limit' that isn't exposed in the input schema, creating an incomplete picture. It also doesn't mention error conditions or edge cases, which are minor gaps for a read-only tool.

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 fully compensates by explaining each parameter: label (with default), match (with a PromQL example), and target (with 'omit for default'). This adds substantial meaning beyond the bare schema, making it easy for an agent to understand what each parameter does.

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 clearly states the tool's function: enumerating distinct values of a label (defaulting to __name__ for metric names). It uses a specific verb ('enumerate') and resource ('label'), and mentions PromQL and Prometheus target, which distinguishes it from similar sibling tools like loki_label_values.

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 provides clear context for how the tool works (optional selector, target, truncation), but does not explicitly mention when to use this tool over alternatives. It lacks direct guidance such as 'use this for Prometheus metrics, not Loki labels', and there are no exclusions or alternative tool references.

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