series_metadata

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

With no annotations, the description carries the full burden. It discloses read-only intent via '[READ]', explains the return envelope with returned/limit/truncated, and explicitly warns that truncated results are partial, advising corrective action. This is solid but doesn't cover every possible behavior like auth or error conditions.

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 well-structured: a one-line purpose, a concise Args list with four entries, and a short return note. No filler; each sentence adds useful information. The front-loaded [READ] tag immediately communicates the operation type.

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 no output schema, the description does the necessary work of explaining the return envelope and the critical truncation caveat. It also covers all parameters. It could give a bit more detail on the exact shape of the metadata entries, but 'label-set metadata' conveys the core idea, so it is sufficiently complete.

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?

The schema has no parameter descriptions (0% coverage), but the description fully compensates: match is explained with a PromQL example, start and end specify format (RFC-3339 or unix timestamp), and target clarifies it is the Prometheus target name with a default. Every parameter gets meaningful semantic detail.

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 opens with '[READ] Series (label-set) metadata for a PromQL selector,' which clearly identifies the verb (read), resource (series metadata), and input (PromQL selector). This distinguishes it from data-returning tools like instant_query or range_query and from 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 gives concrete guidance on using the tool: what match is, optional start/end, and target selection. It also advises how to handle truncated results. However, it does not explicitly state when not to use this tool or name alternatives, so it falls short of the highest bar.

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