admissibility_preconditions

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It explains that the tool checks three store-scoped invariants, creates no new statuses, and reports `applicable: false` when a precondition cannot apply, which does not count as holding. This meaningfully describes behavior, though it could further clarify whether the check is purely read-only and what the full success response 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?

The description is well-structured with a clear opening question, a positional statement, bulleted invariants, and a behavioral note. However, the final paragraph containing the issue reference and the 450-record store example is historical provenance that an AI agent does not need in order to select or invoke the tool correctly. It adds noise to an otherwise focused definition.

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 that there are no parameters, no annotations, and no output schema, the description does a solid job of explaining what the tool checks, what the invariants mean, and how `applicable: false` should be interpreted. It could be more complete by describing the exact return shape when invariants hold, but the core context needed for correct invocation is present.

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 and an empty input schema, so there are no parameter meanings to clarify. The description instead adds value by explaining what the check examines, which is appropriate for a no-argument diagnostic. This meets the baseline for a zero-parameter tool.

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 role: checking whether the store is in a state where an applicability question can be answered at all. It names the specific resource (store-scoped invariants), lists the three invariants explicitly, and distinguishes itself from evaluate_applicability, which is the layer above. This strongly differentiates the tool from its siblings.

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 context by positioning this tool as 'the layer BELOW applicability' and contrasting it with evaluate_applicability: evaluate_applicability asks about a record's admissibility, while this tool asks whether the supporting machinery works. This effectively tells an agent when to use it, though it stops short of an explicit 'use this when...' or 'do not use when...' directive.

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