web_extract

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

With no annotations provided, the description fully discloses behavioral traits: cost ($0.03 per call), honest User-Agent, robots.txt adherence, redirect limits (3), size caps (1MB input, 100k output), caching (5 min), and error conditions (private/internal targets refused). No contradictions.

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 (three short sentences) and front-loaded with the core action and cost. Every sentence adds necessary detail without repetition. Perfectly structured for quick parsing.

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 single parameter and no output schema, the description covers all essential aspects: input requirements, behavior, caching, caps, and error cases. The tool is simple and the description is fully sufficient.

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 coverage is 100% with a clear description for the 'url' parameter. The description adds value by specifying additional constraints (must be public HTML page, http/https), which enhances understanding beyond the schema.

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 it fetches a web page and extracts readable content as clean markdown, specifying verb and resource. It also lists use cases (research agents, summarization, RAG) which distinguishes it from unrelated sibling tools like crypto_gas or weather_us.

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 explicitly says 'Use for research agents, content extraction, summarization pipelines, and RAG ingestion' and includes constraints (HTML only, private/internal refused). It does not explicitly mention alternatives but the context of siblings shows no overlap, so guidance is clear enough.

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