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

The annotations declare readOnlyHint and openWorldHint, but the description adds significant behavioral context: cost per call, caching behavior, the schema.org + LLM dual-extraction strategy, and the _shopgraph verification metadata (provenance, freshness, abstain). This goes far beyond the structured annotations and helps an agent understand trustworthiness of results.

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 moderately long but front-loaded with a clear purpose sentence, then elaborates on usage, cost, and metadata. Every sentence adds value, though the metadata explanation is dense and may take a couple reads.

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 description thoroughly explains the tool's verification metadata and cost model, but since there is no output schema, it leaves the actual product fields vague ('structured product data' with only price and availability as examples). An agent may not know what fields to expect. This is a notable gap given the lack of an output schema.

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?

All 8 parameters have descriptions in the schema, so the baseline is 3. The description adds context about caching and costs that informs force_refresh and minimum_confidence, and the mention of 'abstain' explains null field behavior, which aligns with confidence-related params. This is enough to elevate slightly beyond baseline.

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 clearly states the tool extracts product data from raw HTML and distinguishes itself by noting 'no HTTP fetch needed', positioning it as a post-scraping enrichment step. It also mentions the use of schema.org + LLM fallback, making its function specific.

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 says 'Ideal when using Bright Data, Firecrawl, or any scraping API — pipe the HTML through ShopGraph for structured product data', providing clear context for when to use this tool. While it doesn't name alternative tools or exclusions, the 'raw HTML you already have' phrasing implies it is for pre-fetched content.

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