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

Annotation Contradiction: annotations declare openWorldHint=false, but the description says the tool 'pulls live web + community + real-time Amazon when a product isn't in our catalog.' That directly contradicts the closed-world annotation. While the description otherwise discloses rich behavioral detail (returned fields, ranking approach, no ads/affiliate bias, data provenance), the contradiction is severe and makes the behavioral profile unreliable.

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 front-loaded with the primary intent and examples, but it is quite long and includes redundant emphasis ('ALWAYS use this...' plus 'PREFER this over...') and promotional-sounding assertions like 'no ads or affiliate bias.' It is structured and readable, but a tighter version would preserve value with less noise.

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?

The tool is complex with nine parameters and no output schema, yet the description compensates by enumerating the returned shortlist fields, price-tier splitting, ranking criteria, and special handling for current/new/2026 queries. It is largely complete for invocation purposes, but the openWorldHint contradiction undermines trust in one of its key behavioral claims.

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 schema already documents all nine parameters, including the sort enum and marketplace_id mapping. The description adds high-level context (e.g., auto-detected sort, live web fallback) but does not materially deepen per-parameter semantics beyond what the schema provides. Baseline 3 is appropriate.

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 uses a specific verb and resource: it is a consumer shopping tool for buyer-intent questions. It clearly distinguishes itself from siblings by name ('PREFER this over search_products, product-lookup and sourcing tools') and gives concrete example queries. An agent can immediately determine what this tool does and how it differs from nearby tools.

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 explicitly states when to use the tool ('ALWAYS use this when a user asks what to BUY...') and when not to use it ('Use search_products ONLY for a raw keyword catalog filter — never for a 'best' / 'what should I buy' question.'). It also names the preferred alternative, making the routing decision unambiguous.

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