Hive Ledger — deterministic varroa treatment rotation

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

With no annotations, the description carries the full burden and does well: it declares the evaluation is deterministic with no AI, and it discloses the three result categories including the honest handling of inputs it cannot judge. It stops short of stating that the tool is purely non-mutating/read-only and has no side effects, which is the main unstated behavioral trait.

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 tight sentences, front-loaded with the purpose and followed by the guarantees and outputs. Every clause carries information — determinism, rule basis, and result categories — with no filler.

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?

There is no output schema and no annotations, so the description must explain returns, and it does name the three return categories. For a 3-parameter deterministic analysis tool this is close to complete, though the shape/severity of individual violations and cautions is only described at a high level.

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%, so the baseline is 3, but the description adds real linkage: naming 'same active group repeated' and 'label withdrawal period breached' tells the agent that irac_group/active must be populated and that label_withdrawal_days is compared against harvest_date. That is meaning beyond the field-level schema text, though the role of infestation_percent is left unstated.

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?

States a specific verb and resource: check a beekeeper's varroa treatment history against published resistance-rotation guidance, then enumerate what the check produces (violations, cautions, unjudged inputs). This is far more than a tautology and an agent can tell what the tool computes. It does not, however, explicitly distinguish itself from siblings like list_active_groups or get_thresholds, which an agent might plausibly reach for when assembling inputs.

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 implies the use case (validating a treatment history against rotation rules) but gives no explicit when-to-use, when-not-to-use, or pointer to sibling tools. It also never says whether a harvest_date or infestation_percent is required for the useful checks, leaving the agent to infer invocation conditions from the schema alone.

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?

No annotations are provided, so the description carries the full behavioral burden. It usefully discloses that the values are quoted from studies with exact sources and include a monitoring cadence, and explicitly says it is not a recommendation. It does not discuss permissions or read-only nature, but for a zero-parameter reference tool that is a minor gap.

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?

Two tightly written sentences with no filler. The core payload is front-loaded, and the important caveat about study values versus recommendations is placed clearly at the end.

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 zero-parameter reference tool with no output schema, the description must explain the return content, and it does: published thresholds, exact sources, and monitoring cadence. The caveat clarifies intent. It could be slightly more explicit about format, but it is complete enough to call correctly.

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 tool has zero parameters, so per the baseline rule this scores 4. There are no parameter semantics to add beyond the schema.

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?

Clearly states a specific resource: published varroa damage thresholds with exact sources plus requested monitoring cadence. The verb is implicit in the tool name, but the content is unambiguous. It does not differentiate from the sibling tools check_rotation or list_active_groups, though the domain is distinct.

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?

Implies usage as a reference lookup for quoted study values, with the caveat 'not a recommendation'. However, it gives no explicit when-to-use guidance, no exclusions, and no alternatives to consider. Adequate but with clear gaps.

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?

With no annotations, the description carries the full burden. It discloses that this table is the reference the service "rotates on," which hints the data is authoritative and static reference data, but says nothing about whether the list is paginated, how often it changes, or what the returned shape looks like.

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?

Two compact sentences with no filler and the resource is front-loaded. The phrasing "the active-group table this service rotates on" is slightly convoluted but does not waste space.

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 zero-parameter reference lookup with no output schema, the description covers the important ground: what the table contains and how the agent should use the returned value. Missing only return-format and freshness details, which are minor for a static reference list.

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 tool takes zero parameters, so per the baseline there is nothing for the description to compensate for. The description's enumeration of the columns (product, active ingredient, IRAC mode-of-action group) is a small bonus for anticipating the response shape.

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 the resource (the active-group table) and enumerates its columns, but it never uses a verb — it never says this tool returns or lists that table. An agent can infer it is a reference lookup, yet the statement of what the tool actually does is implicit rather than explicit, and no sibling (check_rotation, get_thresholds) is named.

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?

"Cite the group number when you record a treatment" gives an implied usage context, which is more than nothing. But there is no statement of when to call this versus check_rotation or get_thresholds, and no exclusions or prerequisites.

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