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

Far beyond the annotations, it discloses the cost model ('Uses one prepaid credit. Never charges a card'), the failure policy ('Failed or degraded evaluations are not billed'), the retention window ('held in memory, readable for up to 15 minutes'), data-processor exposure (Firecrawl, OpenAI; no wallet keys or chat content), redaction rules, and that page-copied content is untrusted. This is consistent with destructiveHint=false not being set and readOnlyHint=false, since a credit is consumed.

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 content is mostly valuable, but it is delivered as one undifferentiated block of roughly twenty short assertions with no grouping or ordering by importance. Redaction details and selector/URL-normalization trivia are interleaved with critical billing and routing facts, so the agent must read the whole paragraph to find the decision-relevant parts.

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 still explains what comes back (checked/not_checked, more.eval_id, keep/rename/fix_description/needs_owner_input statuses), plus prerequisites (prepaid wallet, setup URL) and degradation behavior. An agent has enough to call it correctly and interpret the response.

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 0%, so the description must carry parameter meaning, and it only covers `url` ('the page to audit,' plus query/fragment handling rules). The second parameter, `max_offers` (1-12), is never mentioned, so an agent has no idea what it caps or how to set it.

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?

First sentence gives a specific verb, resource, and scope: 'Evaluate one public HTTPS page URL for WebMCP readiness,' then enumerates the outputs (observed tools, recommended tools, do-not-expose). It also fences the tool off from adjacent misreadings ('not a generic HTTP client and not a third-party REST wrapper'), so an agent can classify it without opening the schema.

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?

It states the exclusions ('does not run site tools and does not read llms.txt or /.well-known') and routes the agent to the sibling: 'fetch the rest with get_eval_part' with the triggering condition (result too large). It never explicitly says when to evaluate a page vs. skip evaluation entirely, which keeps it short of a 5.

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