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

With no annotations, the description carries the full disclosure burden and does well: it explains the ordering convention (strongest growth first, steepest contraction first), the monthly cadence, the response measure scale (+1 to -1, 0 = no change), and cautions that this is a derived breadth measure rather than an index level. It does not cover data availability lag or null/missing-month behavior, but the core semantics are disclosed.

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?

Two dense but well-ordered sentences: the first front-loads what the data is and its structure, the second delivers the measure semantics and the key caveat. Every clause carries information; the only cost is that the sentences are long and the parenthetical list of question dimensions packs a lot into one breath.

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 three-parameter read-only tool with no output schema and no annotations, the definition gives enough to call it correctly: data source, granularity, ordering, measure scale, and a caveat against index-level interpretation. Only return-shape details (number of months returned, formatting) and default behavior are left implicit.

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, and it largely does: it explains the survey split (18 manufacturing vs 18 services industries) and enumerates the question dimensions (overall/headline, new orders, production/business activity, employment, prices) that the enum encodes, plus the monthly framing that motivates the months parameter. It does not explain the months range (1-24) or that 'headline' means the composite, leaving minor gaps.

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 (US industry breadth from the ISM Report On Business rankings) and states exactly what it returns: which of ISM's 18 manufacturing or 18 services industries reported growth or contraction per month. It is clearly distinct from siblings like get_sectors or get_nowcast by domain, though it never explicitly names an alternative to differentiate against.

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?

Usage is implied rather than stated: a reader infers this is the tool for ISM breadth/rank data and is warned it is a 'derived breadth measure, not an index level', which is a useful disambiguation constraint. There is no explicit when-to-use or when-not-to-use guidance relative to get_sectors, get_nowcast, or get_aggregate.

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