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

Annotations already establish that this is a safe, idempotent, closed-world read, so the bar is lower. The description still adds real value beyond them by disclosing the caching subtlety — it returns the versioned live snapshotPolicy when cached tool descriptions differ — which an agent could not infer from the annotations or the empty schema.

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 sentences, front-loaded with the payload list and closed with the actionable trigger, so the most decision-relevant content comes first. The snapshotPolicy clause is dense but earns its place as the one non-obvious behavioral detail.

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?

An output schema exists, so return shape needn't be spelled out, and the description instead covers what an agent needs for the decision: what signals come back and when to call. For a zero-parameter read-only probe, nothing material is missing.

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 the description carries no parameter burden and the baseline of 4 applies. The schema is empty and fully consistent with a no-argument probe.

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 a specific capability probe and enumerates concretely what it reports: Blender execution availability, compute budget usage, open job counts, and deployment policy limits. That set of return signals distinguishes it clearly from siblings like deployments_list, jobs_list, or asset_generate_status, which each cover only one slice.

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?

It gives an explicit trigger: "Call before queueing work to fail fast," which tells the agent precisely when this probe pays off relative to render_create, scene_apply, and other queueing tools. It does not state when-not to call or name an alternative for narrower questions, which keeps it short of a 5.

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