set_pattern_state

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

With no annotations, the description carries the full behavioral disclosure burden. It does this well by explaining partial-update behavior, the meaning of freeze, strengthen, layer ordering, and the performance tradeoff of particle_distance. It does not discuss potential re-simulation needs or interactions between parameters, but it discloses the main side-effect profile clearly.

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 front-loaded with the core behavior and the pin caveat, then cleanly organized into an Args list. It is efficient with no filler. A few arg explanations are terse or slightly tautological ('solidify: Solidify'), but the overall structure is easy for an agent to parse quickly.

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 9-parameter tool with no annotations, no output schema, and zero schema descriptions, the description is remarkably complete: it defines every parameter, explains the update semantics, and gives the key constraint about pins. It omits interaction/dependency details such as whether solidify_strength only matters when solidified is true, and it does not mention return behavior, but these are not critical for a correct initial invocation.

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 0%, so the description must compensate, and it fully does. Every one of the nine parameters receives an explanation beyond its schema title, including units for particle_distance (mm), units for grain_degrees, and the practical meaning of layer ordering. This is exactly the kind of semantic enrichment the input schema lacks.

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 opens with a clear verb+resource: 'Set simulation state of a pattern piece,' immediately identifying the tool's scope and target. It goes beyond a bare restatement by explaining that only passed values are changed, which clarifies partial-state mutation. It does not explicitly differentiate itself from sibling tools like get_pattern_state or set_simulation_quality, so it falls just short of a 5.

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 meaningful usage context: 'Only the values you pass are changed' and the explicit note that CLO's API cannot place pins, so freeze or strengthen should be used instead. This gives an actionable hint for a common scenario, but it does not provide broader when-to-use vs when-not-to-use guidance relative to the many sibling pattern/fabric tools.

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