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

Annotations already establish this as a safe read (readOnlyHint=true, destructiveHint=false, openWorldHint=false), so the bar is low. The description still adds two useful behavioral facts: results are 'public' details only, and returned URLs must be preserved exactly, which is a non-obvious output-handling constraint not encoded anywhere else.

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?

Two short sentences: the core action first, the URL-preservation caveat second. No filler, nothing repetitive, and the important operating rule is front-loaded rather than buried.

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 single-parameter read tool with an output schema, a complete annotation set, and full schema coverage, the description covers what is needed. It leaves only the company-vs-organization sibling distinction unaddressed.

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 the schema field already documents that the slug is the value returned by search_companies. The description only restates 'by slug' without adding format, case-sensitivity, or validity details, so the 3 baseline for fully documented parameters applies.

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 specific verb and resource ('Get public details for one AI company by slug'), which is clear and actionable. It does not, however, differentiate itself from the closely named sibling get_organization, leaving an agent to infer how 'company' differs from 'organization' in this dataset.

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?

Saying 'by slug' and having the schema note that the slug comes from search_companies implies a search-then-fetch workflow, but the description itself states no when-to-use condition or alternative. Usage is inferable rather than explicit.

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