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

Annotations already cover read-only and non-destructive behavior. The description adds valuable behavioral context: extraction is deterministic via regex, involves zero LLM calls and zero API keys, and has a known cost ($0.01/call, x402 USDC). This goes beyond the annotations and helps the agent anticipate operational characteristics.

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 main purpose, followed by method, target audience, and pricing. It's about 70 words and every segment adds context. The pricing and audience details, while useful, extend it slightly beyond the crispest possible form, so a 4 is appropriate.

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?

Given no output schema, the description explains the return format ('structured JSON') and enumerates the fields, which is sufficient for output understanding. It also covers use cases, cost, and method. It doesn't address potential failures or edge cases, but for a deterministic extractor with robust schema, this is a minor gap—hence a 4.

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 parameters are fully documented there. The description adds minimal parameter-specific meaning beyond what the schema provides—it lists the extractable fields in prose, which reinforces the 'fields' parameter but doesn't introduce new semantics. This matches the baseline for high coverage.

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 ('Turn') with a clear resource ('any URL') and outcome ('into clean structured JSON'), listing the exact data points extracted (title, description, image, JSON-LD, headings, links, emails, prices). It distinguishes from siblings like url-to-markdown by explicitly emphasizing structured JSON and deterministic extraction via regex.

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 clearly contextualizes when to use: 'Built for AI agents that need one page turned into typed data, cheaply.' It also notes zero LLM calls/API keys, implying a use case where deterministic, low-cost extraction is preferred. However, it doesn't explicitly name alternatives or when-not-to-use scenarios, so it's one point short of a perfect 5.

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