SakuttoWorks-Data-Normalizer

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

Without annotations, the description carries significant weight, disclosing critical behavioral traits including web scraping protection bypass capability, LLM context window optimization, sanitization of content, and support for PDF inputs. Missing operational details like rate limits or error handling prevent a 5.

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 efficiently packs purpose, optimization details, and use cases into two sentences with good front-loading. The all-caps 'CRITICAL USE CASES' is slightly unconventional but does not significantly detract from readability or structure.

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 the lack of annotations and output schema, the description adequately covers the tool's capabilities but omits operational constraints such as authentication requirements, rate limits, timeout behavior, and error response patterns necessary for confident invocation.

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?

With 100% schema coverage, the baseline is 3. The description adds value by contextualizing the 'fields' parameter regarding token minimization for LLMs and the 'url' parameter regarding PDF support, exceeding the schema's basic descriptions.

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, sanitizes, and normalizes web content into Markdown or JSON, specifying the transformation with concrete verbs. It distinguishes itself by mentioning LLM optimization and PDF handling, though the specific focus on Japanese use cases is oddly narrow.

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 'CRITICAL USE CASES' section explicitly lists scenarios where this tool excels, including bypassing scraping protections and handling academic papers, providing clear context for when to invoke it. However, it lacks explicit warnings about when not to use it or prerequisites like required authentication.

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