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

With no annotations, the description carries the full burden and does well: it declares 'Pure DB read — no LinkedIn pull, no credits charged', covering both safety and cost implications, and discloses the `_unknown` bucketing edge case. It omits anything about permissions or result size/pagination, keeping it from a 5.

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?

Four tight sentences, each doing distinct work: purpose, pivot source, depth semantics, unknown handling, and cost/safety. The most decision-critical information (what the tool produces) is front-loaded with zero 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?

There is no output schema, but the description conveys the return shape well ('histogram' with sample company names attached per row) and all four parameters are documented in the schema. For a read-only aggregation tool with no annotations, only the lack of permission/scope context and row-limit behavior leaves a gap.

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%, so the baseline is 3, and most of the description's depth explanation duplicates the schema's own 'depth' documentation almost verbatim. The `sample_companies_per_bucket` parameter is never mentioned in the description beyond the vague 'sample company names... per industry row'.

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?

It opens with a specific verb+resource ('Histogram of engagers' companies grouped by industry') and names the exact data source (companies.industry_hierarchy), which clearly separates it from list-style siblings like get_post_engagers_by_company. It stops short of explicitly naming the sibling it is not, so the differentiation is inferential rather than stated.

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 explains how the depth parameter changes the bucketing and how missing data is handled, which implicitly guides usage. But it never says when to reach for this tool instead of get_post_engagers_by_company, get_company_engagement_overlap, or get_engagement_trends, and gives no prerequisites or exclusions.

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