Get LLM Monitor Trend

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

Annotations already provide readOnlyHint and openWorldHint, so the description correctly reinforces 'Read-only' rather than contradicting it. It adds valuable context beyond annotations: it reads previously collected LLM Monitor data, does not trigger new runs, and requires an API key. This exceeds the minimum bar for behavioral disclosure.

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 longer than the examples but every sentence carries functional weight: purpose, invocation modes, behavioral scope, and auth requirement. It is front-loaded with the core purpose and avoids filler, though it could be slightly tightened without losing information.

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?

For a read-only tool with one optional parameter and no output schema, the description covers the essential operational context: what data is read, what the two call patterns return, and the API key requirement. It does not mention pagination, error cases, or exact formatting of the project id, but these are minor for this simple 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?

Although schema description coverage is 100%, the description meaningfully enriches the sole parameter. It explains the omission behavior (list all monitored brands) and the provided behavior (full trend, per-provider breakdown, competitor comparison, average position, example answers) — details far beyond the schema's one-line parameter description.

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 states a highly specific verb and resource: seeing whether AI assistants mention a brand and how that share-of-voice trends versus competitors. It also clearly distinguishes the two modes of operation (list all vs. detail one), making it easy to tell apart from the unrelated sibling tools.

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 instructions on when to call with no argument versus with a brand name/project id, and clarifies that it does not trigger new runs. It does not explicitly name alternatives or exclusion conditions, but the sibling tools are thematically distinct enough that no routing ambiguity arises.

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