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

The description discloses critical behavioral traits beyond the annotations: counts are from a mirror not the office, coverage varies, and stale offices are flagged in 'do_not_quote'. It even warns against misinterpreting low counts. This adds significant context that annotations alone (readOnly, openWorld, idempotent) do not convey, and there is no contradiction.

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 well-structured: it starts with the core purpose, then usage, then a critical caveat. Every sentence contributes value, and the caveat is prominently flagged with uppercase and 'do_not_quote'. It is not overly long for the complexity of the tool, and the formatting aids readability.

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?

Given the tool is a read-only analytical tool with no output schema, the description covers the necessary context: what it returns, how to invoke it, and the important mirror/coverage caveat. It also instructs the agent on how to relay the information ('Relay that'), making it sufficient for the agent to use the tool correctly.

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 carry the parameter semantics. It explains that nice_class is a numeric class (with examples), jurisdiction is an office code ('EM', 'US', 'GB', 'CH'), and months is a window. This adds meaning beyond the bare schema. However, it does not mention the default values (months=12, jurisdiction=''), which would have made it more complete.

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 clearly states the tool's purpose: 'Filing activity in a Nice class — volume by month and the busiest filers.' This goes beyond a simple restatement by specifying the resource (Nice class), the output (volume by month, busiest filers), and the scope, distinguishing it from sibling tools like opposition_window.

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 description provides concrete usage instructions: 'Pass a Nice class (9, 42, 36…), optionally an office code ('EM', 'US', 'GB', 'CH'), and a window in months.' It also states what questions the tool answers, giving clear context. It does not explicitly mention when not to use it or compare with alternatives, but the use cases are well-defined.

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