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

With no annotations provided, the description carries the full burden and does so thoroughly: it discloses deterministic heuristics, lack of semantic understanding, labeled output, sandbox/testnet semantics, free testing, and Ed25519-signed deliverables. This is far beyond a minimal description.

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?

Three dense sentences with no filler. The core action is front-loaded, the critical non-LLM caveat is immediate, and the sandbox/signing notes are concise and relevant.

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?

The tool has no output schema and no annotations, and the description does not specify the exact output structure beyond 'plainly labeled heuristic.' The auth/payment flows are only present in the schema, and the description leaves the reader to infer output format and operational details.

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 only 50% and the description adds some meaning by mapping 'text' and 'fetched page' to the input object, but it does not detail parameter behavior. The quote_id parameter is well described in the schema itself, so the description's compensation is partial rather than complete.

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 opens with a specific verb and resource: turning messy text or a fetched page into structured data. It explicitly names the extraction targets (headings, links, tables, key statistics) and distances itself from LLM extraction, making its purpose and scope unambiguous.

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?

It clearly states when to use the tool (messy text or a fetched page) and what it is not for (not LLM extraction, no semantic understanding). It does not explicitly compare against sibling tools, but the exclusion is strong enough to guide selection.

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