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

With no annotations, the description carries the full behavioral burden and does well: it discloses that output is pre-generated ranked hypotheses, each containing a supporting signal, a falsifiability check ('what else would be true'), and a next-tool recommendation, plus an explicit caution against asserting causation. It doesn't cover auth, rate limits, or pagination, but the causal-safety framing is meaningful context.

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?

Four tight sentences, front-loaded with the core purpose and followed by return-structure detail and trigger conditions. Every sentence carries distinct information; nothing is redundant.

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 tool with no output schema and no annotations, the description does the heavy lifting by explaining what the return contains (ranked hypotheses with supporting signals and next steps). Combined with fully-documented params, an agent has enough to call it correctly, though the absence of any safety/auth profile leaves a minor gap.

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 the schema already documents all five parameters (store, dates, comparison enum, pagination offset). The description adds no syntax or format detail beyond the schema, so the baseline of 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?

States a specific diagnostic purpose ('why did revenue change?') and enumerates exactly what it bundles: headline metric deltas, product mix shifts, and ranked hypotheses. It clearly differentiates itself from siblings like get_metrics_comparison or get_anomalies by positioning as the consolidated causal-diagnosis tool rather than a raw signal source.

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?

Gives explicit trigger conditions — 'Use when the merchant asks why revenue went up or down, what's driving performance, or what changed.' This clearly frames the when-to-use context. It stops short of naming a specific alternative tool or stating when-not to use it, which keeps it from a 5.

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