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

With no annotations and no output schema, the description carries the full burden. It does disclose one behavioral trait — results preserve the order of the given values — but says nothing about return shape, the 10-value cap, dataset scoping/permissions, or whether all dataset columns are searchable. That leaves most behavioral traits undisclosed for a zero-annotation tool.

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?

A single compact sentence that front-loads the dataset and the selection rule with no filler. It loses a point only for being a dangling fragment (no verb) that reads awkwardly rather than as a crisp instruction.

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?

The tool is simple (two required params, no nesting, no output schema), and the description covers the core selection logic well enough to call it. It still leaves gaps around what a comparison actually returns ('rows side by side' is only in the title) and the value-count limits, so it is adequate rather than 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?

Schema description coverage is 0%, so the description must compensate. It does convey the key relationship between the two parameters: 'column is any of the given values' tells the agent the match is OR-semantics over the values array, which the bare schema does not. It still omits the minItems=2/maxItems=10 bounds and any notion of how the 'order given' maps to the values array.

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 specific resource (rows of the PropFirmPicker dataset) and the selection rule (column matches any of the given values, returned in given order), which goes beyond a tautology. However it is a verbless fragment that never states what the tool actually does as an action, and it does not contrast itself with siblings like dataset_search or dataset_row beyond the vague 'X vs Y' framing.

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 trailing clause 'for "X vs Y" questions' gives an implied usage context, telling the agent this is for comparison-style lookups. There is no when-not guidance, no prerequisites, and no named alternative (e.g. dataset_search or dataset_row) to route between, so the guidance stays at the implied level.

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