Web fetch

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

With no annotations, the description fully discloses key behaviors: default output format, JS rendering, captcha solving, geo-targeting, cost structure (base 1 credit, multipliers for difficulty), caching via expiration, and a link to live pricing. No destructive behavior is indicated, which is consistent with a fetch tool.

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 compact yet comprehensive; it uses bullet-style formatting for cost multipliers to improve readability. Every sentence provides essential information without redundancy.

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 8 parameters, 1 required, and no output schema, the description sufficiently covers what the tool returns (Markdown by default, with alternatives), how caching works, cost implications, and anti-bot handling. It is complete enough for an agent to invoke the tool correctly without additional information.

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%, and the description adds significant value beyond the schema: it explains why Markdown is best for LLM, how difficulty multipliers work, that expiration=0 ensures live data, and that geo-targeting parameters (country/city) do not change cost. It also clarifies that subdivision is ignored when city is set.

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 uses specific verb+resource: 'Fetch any URL through Massive.' It clearly states the primary function and distinguishes from sibling tools like web_search, which searches the web rather than fetches specific URLs.

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 explicit guidance on format selection (Markdown best for LLM), difficulty escalation (start low, use higher if low fails), and expiration settings (0 for live data). It also explains cost multipliers. It could explicitly mention when not to use this tool (e.g., use web_search for general queries), but the context is sufficient for appropriate usage.

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