extract_data

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

No annotations are provided, so the description carries the full burden. It transparently discloses that the tool makes external API calls to OpenAI (requiring OPENAI_API_KEY), saves results to extracted.jsonl, and includes LLM attribution metadata. It also mentions token cost implications for sample_size. Missing details on error handling or rate limits, but overall provides good behavioral insight.

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 well-structured and appropriately sized. It starts with a concise summary sentence, followed by a paragraph on how it works, a phrase on use cases, and a clear bullet-like list for arguments. Every sentence adds value, with no redundant or irrelevant content.

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 tool complexity (4 parameters, all optional, an output schema exists), the description is complete. It explains auto-discovery when fields are empty, the role of context, and environment requirements. Despite not detailing the output schema content, the presence of an output schema reduces the need for that detail. The description covers all essential behavioral and usage aspects.

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%, yet the description thoroughly documents all four parameters in a dedicated 'Args' section: jsonl_path (full path, default location), fields (comma-separated, example, auto-discovery behavior), context (purpose, ignored when fields specified), and sample_size (default, effect on quality and cost). This fully compensates for the lack of schema 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's purpose: 'Extract structured fields from crawled pages using an LLM.' It uses a specific verb ('extract') and identifies the resource ('crawled pages') and method. It also lists use cases (competitive research, API documentation analysis, building structured datasets), effectively distinguishing it from sibling tools like crawl_site, list_pages, read_page, and search_pages.

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 provides clear usage context: 'Use this for competitive research, API documentation analysis, or building structured datasets from unstructured web content.' It implies when to use the tool but does not explicitly state when not to use it or suggest alternatives among sibling tools. Thus, while helpful, it lacks explicit exclusions.

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