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

With no annotations, the description carries the full burden and does disclose meaningful traits: the return shape ('per-page cleaned text with metadata') and cost ('$0.003 per page crawled'). It omits operational details such as authentication requirements, error/partial-failure behavior, or latency expectations, which is the remaining gap.

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 tight sentences with zero filler, front-loading what the tool does, then when to use it, then return shape and price. Each sentence earns its place and the important routing information comes first.

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?

No output schema exists, so the description rightly covers the return value, and it also notes cost. For a five-parameter tool with a dual query/urls mode, it is nearly complete, though it could say more about what happens when both query and urls are supplied or omitted.

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 every parameter is already documented in the schema, including the query-vs-urls distinction and the markdown/text enum. The description adds no parameter-level meaning beyond that, making the baseline 3 correct.

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 ('Fetch web pages and convert them into clean LLM-ready text') plus the delivery target ('RAG pipelines'). This clearly distinguishes it from the sibling scrapers, which are all platform-specific (instagram_scraper, youtube_scraper, etc.), while this one is the generic page-content fetcher.

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 usage condition: 'Use when an agent needs readable page content from the web without HTML noise — a lightweight browser alternative for retrieval.' It frames the retrieval use case well but names no excluded cases and points to no alternative tool for adjacent needs (e.g. structured scraping), so an agent still infers the boundary against siblings.

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