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

Annotations already declare readOnly, idempotent and non-destructive, so the description adds genuinely new behavior: the four data sources checked, the pass criteria (every check with data passes, at least two have data), the UK 250+ staff qualifier, and that a badge image link is returned.

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 purpose and the user question, then enumerates the checks compactly. Slightly dense in the middle but every clause carries meaning.

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, the description usefully previews the return content (per-check figures plus a badge image link) and the qualification rule. Coverage is good for a one-param read tool, though it doesn't mention response shape details or error behavior.

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 there is a single required 'company' parameter with an example in the schema, so the description need not explain it; baseline 3 applies since it adds no parameter meaning beyond the schema.

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 (check) and resource (an employer against the fair hiring badge), and frames the user question it answers ("is this employer fair to applicants?"), which cleanly separates it from siblings like employer_reply_rate or company_lookup.

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 "is this employer fair to applicants?" framing tells the agent the context in which to reach for this tool, but it names no sibling alternatives or conditions for choosing them over this one.

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