scrape_docs

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

With no annotations provided, the description must disclose behavioral traits. It mentions creating files and fallback behavior, but does not state if it overwrites files, requires network access, or handles errors. This leaves uncertainty about side effects and preconditions.

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 concise with three sentences, front-loading the primary action. It avoids redundancy and is well-paced, though a slightly more structured breakdown of parameters would improve clarity without increasing length significantly.

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 6 parameters, 0% schema description, and no annotations, the description is incomplete. It does not cover parameter semantics or crucial behavioral details needed for correct invocation. The tool has high complexity that is not matched by the description.

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 explain parameters. It only implicitly mentions config_path through the config context, but fails to explain unlimited, enhance_local, skip_scrape, dry_run, and merge_mode. This is insufficient for an AI agent to use the tool correctly.

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 scrapes documentation to build an LLM skill, specifying output files (SKILL.md and reference files). It distinguishes from siblings by mentioning single-source and multi-source configs, which is not typical of other scrape tools like scrape_codebase or scrape_pdf.

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 mentions automatic detection of llms.txt for faster processing and fallback to HTML scraping, giving some context on when the tool performs best. However, it lacks explicit guidance on when to use this tool versus alternatives like scrape_generic or scrape_codebase.

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