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

Annotations mark this as non-readOnly and non-idempotent, but the description deepens this by stating it 'Creates a Kindwise identification and counts against the per-IP trial quota (default 10 calls per rolling 24 hours)' and that 'No client API key is required or accepted.' It also exposes a critical limitation—'This server cannot see chat attachments unless you copy bytes or a fetchable URL here'—which is exactly the kind of behavioral context agents need. No contradiction with annotations.

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?

Though longer than average, every sentence earns its place. The purpose is front-loaded, followed by input requirements, optional parameters, behavioral effects, constraints, and a follow-up instruction. The structure is logical and scannable—no filler, and the key scoping fact ('vascular plant') appears first.

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?

For a tool with 8 parameters, external dependencies, quota limits, and a follow-up action, the description is remarkably complete. It covers when to use it, how to format inputs, what optional parameters do, what the output looks like, what side effects occur, what fails, and what to do after. An agent has everything needed to call this correctly and safely.

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%, but the description adds meaningfully beyond the field docs: it recommends '2–3 sharp close-ups of leaves, flowers, or fruit', notes that latitude/longitude 'improve ranking for wild plants', explains the health values in context, and gives a concrete tip about Wikimedia 424 failures. These are insights not present in the schema, lifting the parameter guidance from baseline to exemplary.

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 concrete verb ('Identify a vascular plant from photographs') and names the underlying service (Kindwise Plant.id). It enumerates the covered plant types (houseplant, tree, wildflower, grass, crop, weed) and the outputs (scientific name, common names, taxonomy, optional health), clearly distinguishing it from sibling tools like identify_insect and identify_mushroom by the 'vascular plant' scope.

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 gives an explicit 'Use when...' condition tied to the user providing a photo and needing identification. It further instructs when to set health ('when the user also wants disease or pest assessment'), how many images to prefer, and how to handle common failures (e.g., 'Wikimedia often fails with HTTP 424'). It even routes the agent to submit_feedback after the trial, leaving no ambiguity about workflow.

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