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

Annotations cover the read-only profile, but the description adds substantial context beyond them: it enumerates the check categories, the concrete issue codes, and the crucial caveat that the write path auto-repairs issues and enforces invariants this audit skips. This tells the agent exactly what a clean result does and does not guarantee.

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?

Front-loaded with the action and scope, then the return shape, then the caveat. Dense but each clause carries information; the return-shape detail is justified because no output schema exists. Slightly long but no wasted sentences.

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?

With no output schema, the description fully covers the return contract (ok, counts, issue arrays, code/message/item_uids) and closes the semantic gap between validation success and write success. An agent has everything needed to call and interpret it.

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% and the single model_id parameter is richly documented in the schema (user-scoped vs model-scoped key behavior). The description adds no parameter-level detail, so the baseline 3 applies.

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?

Specific verb+resource ('core model validator') scoped precisely to 'structural / formula / timeline checks' and explicitly contrasted with the write path. An agent can distinguish this audit from siblings like layerz_get_model or layerz_diff without opening a schema.

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?

Usage is implied rather than stated: 'without writing' and the caveat that 'ok: true is a sanity signal rather than a write guarantee' suggest pre-write checking, but there is no explicit when-to-use or when-not-to-use versus siblings. No alternatives are named.

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