HortOS (United States)

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 and openWorldHint=true, so the agent knows this is a safe read operation with potentially incomplete results. The description adds useful context about the data scope (live market, USD ex tax) but doesn't disclose return format or pagination behavior. With annotations covering the safety profile, a 3 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?

Three sentences with zero waste. The core purpose is front-loaded, usage examples are given, and the parameter-relevant detail (botanical/common names) is included. 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?

Complete for a search tool with readOnly and openWorld annotations. The description covers what the tool does, what data it returns (growers, pot sizes, prices), and how to use it. The only minor gap is not describing the return format, but with no output schema and a simple search use case, this is acceptable.

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%, so the schema already documents all three parameters. The description adds context about what the query means ('Plant name, botanical or common') and the price format (USD, ex tax), but doesn't add significant syntax or format details beyond the schema. Baseline 3 is correct.

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 the tool's purpose: searching America's live wholesale plant market for which growers list a plant, pot sizes, and trade prices. It uses specific verbs ('Search', 'use it for') and distinguishes itself from the sibling tool by focusing on plant search rather than price list management.

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 clear usage context: 'who grows X' or 'what does X cost wholesale', and notes that botanical or common names both work. It doesn't explicitly exclude alternatives or mention when not to use it, but the context is strong enough for an agent to select it appropriately.

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