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

Annotations carry only readOnlyHint=true, so the description shoulders most of the burden and does add real behavioral context: the preliminary/solo flagging rules, the author-effect removal, and the caller-sensitive visibility rule ('public callers get bands and pooled lifts, the same as Discover'). It does not cover pagination or response shape, but the access-level disclosure is genuinely useful.

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 core deliverable is front-loaded before the alternative-use sentence, which is the right ordering. But the body is a single dense paragraph of stacked parentheticals and undefined internal jargon ('page fit', 'fitted people', 'peopleCounted'), which is hard to parse rather than economical.

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 no output schema in play, the description rightly spends its words explaining what comes back – rollup metrics, the preliminary and solo flags, baseline comparison, category lift – so an agent knows what to expect. Undefined domain terms are presumably covered by the methodology sibling, so the remaining gap is minor for a tool of this complexity.

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 100%, so the schema already documents all six parameters, including the industry/companyIds/workspaceId fallback chain. The description restates the industry-vs-custom-set split but adds no syntax, format, or precedence detail beyond what the schema states. Baseline 3 is appropriate.

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 concrete analytical product: a per-company rollup with population-vs-baseline and author-effect-removed category lift, scoped to an industry or a custom company set. That is well beyond a restatement of the name. However it never differentiates itself from the overlapping siblings category_breakdown and company_fit_summary, so an agent must guess which of the three to reach for.

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?

It gives two concrete triggering questions ('what works in fintech', 'where does X sit in its market'), which map onto the two halves of the output. That is clear usage context. There are no exclusions or explicit routing away from category_breakdown/company_fit_summary, so it stops short of a 5.

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