Aggregate statistics over a period

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

Annotations already declare readOnlyHint and idempotentHint, but the description adds crucial behavioral context: results cover only auctions changed in the scanned window, not the whole catalogue, and instructs to 'Always report the window and coverage alongside any number.' This exceeds the baseline safety hints and prevents misuse.

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 average but every section earns its place: when-to-use, key parameters, examples, returns, and a critical caveat. Clear formatting with headers and bullet-like structures makes it scannable without redundant filler.

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?

With 8 parameters and no output schema, the description thoroughly covers return structure (totals, status histogram, soldSharePercentOfSettled) and the critical sampling caveat. It provides enough information for an agent to correctly select, invoke, and interpret results, leaving no major gaps.

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 covers 100% of parameters, but the description adds substantial meaning: explains 'since / until' omission behavior, 'scan_pages' default and sampling trade-off, and clarifies group_by semantics with examples. This goes far beyond the schema's field descriptions.

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 specific action: 'Counts, price statistics and sold-share over a window of auction activity, broken down by a dimension of your choice.' It clearly distinguishes from siblings by emphasizing 'volume and trends rather than individual lots', with concrete example questions like 'how much land was auctioned last week'.

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?

An explicit 'WHEN TO USE' section lists question types, and the description implies not to use it for single-lot queries (which siblings like get_auction handle). Two example JSON payloads demonstrate realistic invocations, providing clear guidance on when this tool is appropriate.

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