HortOS (Australia)

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

Annotations already mark readOnlyHint and openWorldHint, and the description adds important behavior beyond those: it does not place an order, returns a link on HortOS, and requires the person to review and send before grower confirmation. This meaningfully clarifies the workflow without contradicting the 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?

The description is three focused sentences: core action, return value, and the critical non-ordering caveat. It is front-loaded and every sentence earns its place.

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 simple 3-parameter tool with readOnly/openWorld annotations and no output schema, the description covers the input, the matching behavior, the return link, and the post-call workflow. An agent has enough to invoke the tool and set expectations for the user.

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?

With 100% schema description coverage, the schema already documents job, list, and near effectively. The description adds contextual flavor such as 'growers near the job preferred' but does not materially expand parameter semantics beyond what the schema provides.

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 names a specific verb-resource pair: 'price a whole plant list or planting schedule' from wholesale growers, and states the matching outcome and returned link. It distinguishes itself from sibling search_plants by focusing on list pricing and order hand-off, not plant search.

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 gives clear context for use: the tool accepts a plant list or planting schedule, matches lines to growers, and returns a link for the user to review and send. It does not explicitly say when to prefer this over search_plants, so it misses the top score.

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

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

Annotations already declare readOnlyHint=true, so the description correctly aligns and doesn't contradict. The description adds behavioral context by specifying the geographic scope ('Australia'), the data source ('live wholesale plant market'), and the nature of results (which growers, pot sizes, and trade prices). It also implicitly notes that it returns a list of growers, which is not in the annotations. This goes beyond what annotations provide, so a 4 is appropriate.

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 brief and front-loads the core purpose in the first sentence, then adds usage guidance in the second. Every sentence earns its place. It avoids unnecessary details and is well-structured for quick comprehension by an agent.

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's simplicity (one required param, no output schema) and the annotations covering read-only and open-world hints, the description is fairly complete. It explains the data source, scope, and usage scenarios. It does not mention output format or pagination, but for a search tool without an output schema, this may not be critical. However, a bit more on how results are ordered or what information each result includes could be beneficial, so a 4 rather than a 5.

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?

The schema description coverage is 100%, meaning all three parameters are described in the schema. The description adds semantic value by explaining the purpose of the query parameter ('who grows X or what does X cost wholesale') and notes that common names work, which is not in the schema. It also implies that 'size' and 'state' are optional filters, aligning with the schema but adding context. This pushes beyond the baseline 3.

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 starts with a specific verb ('search') and a clear resource ('Australia's live wholesale plant market'), and immediately clarifies the core use cases: 'who grows X' and 'what does X cost wholesale'. It explicitly mentions both botanical and common name support, and distinguishes itself from the sibling by focusing on market search rather than a price list. This is specific and clearly differentiates from price_plant_list.

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 when to use this tool: 'Use it for "who grows X" or "what does X cost wholesale"'. While it doesn't explicitly state when to use the alternative, it does not need to since the purpose is so clearly scoped. It also mentions that both botanical and common names work, which is a useful usage hint. The context is clear enough that an agent would know when to choose this over price_plant_list.

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