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

Annotations already establish readOnly/idempotent/closed-world, so the safety profile is covered. The description adds real behavioral context beyond that: the corpus size, that results are submit URLs, and an explicit caveat about the meaning of 'unknown' values in the returned data - a data-quality trait no annotation conveys.

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 front-loaded sentences: filters first, output and caveat second. Every clause carries content and nothing is repeated from the schema. It is dense but not padded; the only cost is that the long first sentence packs a lot before the agent reaches the important 'unknown' caveat.

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 9 optional params, an output schema, and full annotations, the description covers enough: what is searched, what is returned, and how to interpret ambiguous values. The remaining gap is routing guidance against related siblings (recommend_launch_directories, get_launch_directory) in an otherwise crowded tool family.

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 baseline is 3. The description still earns a bump by explaining that link_type and pricing filters can yield 'unknown' results and what that means, which is semantic meaning the enum definitions ('dofollow = verified followed link') do not fully carry on their own.

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 names a specific verb (filter/find) and resource (startup, SaaS and AI directories) and quantifies the corpus at 1,667 entries, plus states the output (submit URLs). It stops short of distinguishing itself from the sibling recommend_launch_directories, so an agent must infer the split (raw filter vs. recommendation) from names alone.

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?

It gives useful usage semantics for the result set - unverified link type/price should be treated as unknown rather than free/dofollow - but never says when to choose this over recommend_launch_directories, get_launch_directory, or check_launch_readiness. Usage is implied by the filter list rather than stated.

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