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

The annotations already declare readOnlyHint=true and openWorldHint=false, so no side-effect warnings are needed. The description adds useful behavioral context by noting results are 'ranked by relevance and community engagement' and that matching is semantic. It doesn't go further into result format or pagination, but the annotations carry the main safety burden.

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 sentences carry meaningful information with no filler. The main action and scope are front-loaded, the example follows, and the return value is stated succinctly. Every sentence serves a purpose.

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 read-only search tool, the description covers what it does, how it matches, what filters exist, when to use it, and what the results contain. The param schema is complete, the annotations cover the authorization/side-effect profile, and no output schema exists, so no additional return expectations are missing. It is not a 5 because it doesn't explicitly route away from sibling tools like discover or new_launches, but the description is still fully usable.

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 the input schema already documents every parameter. The description adds context for the primary query parameter ('natural language query', 'semantic matching') and summarizes filter dimensions like pricing, platform, audience, and usage case, which aligns with the schema. This is solid but not exceptional for a fully parameter-documented tool.

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 ('searches') and resource ('PeerPush product catalog') and clearly explains the matching approach: natural language query with semantic matching and structured filters. It also includes a concrete example ('email API for transactional emails'), making the tool's purpose unambiguous and distinguishable from siblings like peerpush_compare or peerpush_trending.

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 gives clear guidance on when to use the tool: when the user wants a product, tool, app, or service recommended for a specific need. It includes a relevant example, which anchors the intended use. It does not explicitly state when not to use it or mention alternatives like peerpush_find_alternative, but the use context is sufficiently clear.

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