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

Annotations already declare readOnlyHint=true and openWorldHint=false, so the safety profile is known. The description adds significant behavioral context: the `computable: false` response leading indicator, the meaning of a broken DAG (CIRCULAR_DEPENDENCY), and the degenerate zero-fill behavior. It also explains the effect of with_values and branch_id on computation. This goes well beyond 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 compact and front-loaded with the core purpose. The second sentence about `computable: false` is valuable but slightly dense; it could be split for readability. Overall, every sentence earns its place, and the structure is efficient.

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 complexity (12 params, no output schema), the description covers the key behavioral edge case (broken DAG) and the main computation flags. It doesn't explain return value structure in detail, but with no output schema, the description could do more to describe what the response looks like. However, the description's focus on the critical failure mode and parameter interactions makes it largely complete for an agent to call it 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?

Schema description coverage is 100%, so the schema already documents all 12 parameters. The description adds some context (e.g., 'with_values=true to compute', 'branch_id lets you audit base-vs-overlay'), but most parameter semantics are already in the schema. Baseline 3 is appropriate because the description doesn't need to compensate for missing schema docs.

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 states a specific verb ('Read') and resource ('model snapshot'), and clarifies it returns items plus optional computed values. It distinguishes itself from siblings by mentioning filtering by UIDs or roles and the optional with_values computation, which is unique among the listed tools.

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 gives clear context on when to use this tool: to read a model snapshot, filter by UIDs/roles, and compute values. It doesn't explicitly name alternatives or exclusions, but the sibling list shows many other tools (e.g., layerz_get_model, layerz_dependencies) and the description's focus on snapshot reading with optional computed values implies when it's appropriate. It lacks explicit 'use X instead' guidance, so not a 5.

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