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

Annotations only cover readOnlyHint/openWorldHint; the description adds genuinely new behavioral context: the two-count semantics (company_count vs profiled_count and which one is safe to size on), the read-back path via find_company_insights(industry:), and the pricing tier ($0.05/call, B2, included in Understanding/Influence). That is material context an agent cannot get from the annotations.

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?

Front-loads the core definition, then the count semantics, then the sibling contrast, then pricing. Dense but nearly every clause carries distinct information; the pricing sentence is slightly tangential but still decision-relevant.

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?

There is no output schema, yet the description explains what a row contains and how to interpret the two counts, names the comparison tool, and discloses cost. An agent has everything needed to call and interpret it 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 coverage is 100% and all three params (page, limit, context) are fully documented in the schema. The description adds no parameter-level detail, so the baseline 3 applies — the schema does the work here.

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?

States a specific verb+resource+scope: 'the demand-side industry rollup: the sectors the profiled companies fall into.' It explicitly contrasts itself with the sibling find_industries ('counts SUPPLY' vs 'this counts DEMAND'), so an agent can distinguish the two without opening either schema.

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?

Gives explicit when-to-use direction ('size a cohort on profiled_count') and names the alternative tool with the condition that selects it ('Comparing the two is how you find a sector with buyers and no sellers'). Nothing is left to inference.

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