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

Annotations already declare the operation read-only, idempotent, and non-destructive, so the safety profile is covered. The description adds value by stating the output shape (name, domain, sector, funding, tech) and the available filter dimensions, which matters because there is no output schema. It does not mention rate limits or empty-result behavior, but those are minor for a read-only query tool.

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 compact sentences: the first states the action, results, and filters, and the second gives a minimal executable example. There is no redundancy, no filler, and the most important scoping information is front-loaded.

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?

Since there is no output schema, the description compensates by naming the returned startup profile fields and providing a concrete invocation example. Combined with a fully-described parameter schema and safety annotations, an agent has enough context to call the tool correctly. Optional details like rate limits and pagination behavior are absent but not critical here.

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 description coverage is 100%, so all eight parameters are already well-documented in the input schema. The description re-states sector, tech, funding, and stealth at a high level and shows an example invocation, but it adds no new semantic detail beyond what the schema already provides. This is an appropriate baseline-3 case where the schema does the heavy lifting.

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 the verb ('Search'), the specific resource ('StartupHub.ai startup database'), and the returned artifact ('startup profiles (name, domain, sector, funding, tech)'), making the core purpose unambiguous. It does not explicitly distinguish itself from sibling tools, but the action and resource are specific enough that it reads as a search/query tool rather than enrichment, investors, or trends.

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 filter list and example imply when to use this tool: when an agent needs to query startup profiles by keyword or filters. However, there is no explicit guidance about when to prefer startuphub_startups over startuphub_enrich, startuphub_investors, or startuphub_trends, and no exclusions are stated. The usage context is clear but must be inferred.

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