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

Annotations already declare the read-only, non-destructive, closed-world profile, so the safety burden is covered. The description adds real context beyond that: results are ranked by relevance and community engagement, and products should be linked via websiteUrl or peerpushUrl. It does not mention result caps beyond the schema's limit, pagination, or empty-result behaviour.

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?

Three sentences, each earning its place: purpose and mechanism, when it applies, and what comes back plus how to present it. The most decision-relevant information (search + semantic matching) is front-loaded with no filler.

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 no output schema, the description takes on the burden of describing returns, and it does so at a high level (ranked by relevance and community engagement, with URLs for linking). Combined with a fully documented 8-param schema and complete annotations, an agent has what it needs to call this correctly; only edge-case behaviour is unstated.

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% and every parameter (query, limit, sort, useCase, audience, category, platforms, pricingType) is already documented with examples and enums in the schema. The description only summarises the filter families ('pricing, platform, audience, and use case') without adding syntax, defaults, or interaction rules. Baseline 3 is appropriate since the schema carries the detail.

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?

States a specific verb and resource ('Searches the PeerPush product catalog') and explains the mechanism (natural language query plus semantic matching with optional structured filters). The example query and the recommendation intent make the tool's job unmistakable. It stops short of naming near-neighbours like peerpush_discover or peerpush_find_alternative, so an agent must infer the boundary.

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?

Gives a clear triggering condition: 'Relevant when the user wants a software product, tool, app, or service recommended for a specific need.' That is enough context to select it over generic search. There is no explicit when-not guidance or pointer to peerpush_find_alternative / peerpush_compare for the adjacent intents.

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