extract_insights

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

With no annotations, the description carries the full burden, and it does disclose the cost ($0.05 USDC on Base) and that this is a paid call — valuable context most tools omit. However it says nothing about payment/auth mechanics, whether the operation is read-only/computational, or what the response actually looks like, and the useful 'price is per CALL, batch aggressively' advice lives only in the schema.

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?

Three short sentences: what it returns, when to pick it, what it costs. The value proposition is front-loaded and every sentence earns 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?

For a paid, single-parameter compute tool with no output schema, the description gives only a loose sketch of the return shape ('highlights and watchouts') and omits payment flow and output structure details. It is adequate to select and call the tool, but not to predict its response.

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 100%, so every field of the single `habits` parameter is documented in-schema, including the batching economics. The description adds no parameter meaning beyond that, so baseline 3 applies.

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 names a concrete deliverable — condensed behavioural highlights, watchouts, patterns, correlations and risk signals — and explicitly distinguishes it from the 'full aggregate breakdown' produced elsewhere. The verb is implicit (no 'extract'/'return'), and no sibling is named, but an agent can infer this is the narrative-summary counterpart to the numeric analysis 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?

'Use when you want the narrative rather than the numbers' is an explicit selection cue that contrasts with the aggregate-report siblings. It lacks named alternatives (analyze_behaviour, analyze_reliability) and any when-not conditions, so it falls short of a 5.

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