datalab_shopping_by_device

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

With no annotations, the description carries the full burden; it discloses the find_category prerequisite workflow but says nothing about the return shape, data source, rate limits, or that this is a read-only analysis. It adds some workflow context but leaves the safety and output profile implicit.

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?

Purpose is front-loaded before the prerequisite, business cases, and example, so an agent can stop reading early. The emoji and the duplicated Korean restatement of the same instructions are minor waste but not harmful.

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 five-parameter analysis tool with no annotations and no output schema, the description should convey what the result looks like (e.g., time-series trend rows by device). The example hints at the question shape but not the returned data, leaving a real 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 coverage is 100%, so baseline is 3; the description earns a bump by glossing the device enum as 'PC vs Mobile', clarifying that 'mo' means mobile, which the schema does not spell out. It says nothing extra about timeUnit or date formats beyond the schema.

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 verb and resource ('Analyze shopping trends by device') and names the comparison axis (PC vs Mobile), which naturally distinguishes it from siblings split by gender, age, or keyword. It does not explicitly name those siblings as alternatives, so a 4 rather than a 5.

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 a concrete prerequisite ('Use find_category first'), three business cases, and a worked example question, which is clear context for when to use it. It stops short of stating when-not to use it or naming the gender/age/keyword siblings as alternatives.

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