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

No annotations are provided, so the description must fully disclose behavior. It mentions 'resolved best-effort' but does not detail rate limits, authentication requirements, error handling, or whether results are read-only. The behavioral transparency is insufficient given the lack of 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?

The description is two sentences long, front-loads the key output fields, then explains input format. Every word adds value; no filler or repetition.

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 simple tool with one parameter and no output schema, the description covers return fields and input formats. It could mention limitations like batch size or error behavior, but the tool has low complexity, so it is largely complete.

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?

The single parameter has 100% schema description coverage. The description reinforces the schema by mentioning both URL and name formats. However, it adds minimal new meaning beyond the schema—'resolved best-effort' is a minor addition. 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 clearly states the tool gets LinkedIn company details and lists specific fields (employee count, industry, website, etc.). It also indicates accepted input types (URLs or names), distinguishing it from sibling tools like get_linkedin_profiles.

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 acceptable inputs but does not provide explicit guidance on when to use this tool versus alternatives (e.g., search_linkedin_employees for employee lists). No when-not-to-use or exclusion criteria are mentioned.

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