adopt model version

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

The annotations are mostly negative (readOnlyHint false, idempotentHint false, destructiveHint false), so the description carries the full burden and succeeds. It discloses that the tool MOVES PRODUCTION TRAFFIC, replaces the alias target, clears any live canary split, stamps adopted_at only on first adoption, and is audit-logged. Error semantics, feature-flag behavior, auth requirements, and response shape are also covered.

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 long but densely informative, with the core purpose front-loaded in the first sentence and high-impact side effects flagged explicitly. Some details like the Zod failure shape are somewhat niche, but they are still useful for error handling. The structure could be tightened, but every sentence adds value.

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 high-risk mutation with no output schema and sparse annotations, the description is unusually complete: it covers purpose, HTTP method, API-key scope, required key level, response format, side effects on canary, error codes, feature-flag behavior, and audit implications. An agent has enough information to decide whether and how to call this tool without needing external docs.

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%, so the baseline is 3, but the description adds meaningful operational context: id must correspond to a deployed version with weights, and aliasName must reference an existing workspace alias. It also maps failure modes to each parameter (400 for undeployed version, 404 for foreign id or unknown alias), which helps the agent validate inputs before calling.

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 and resource — 'Point a model alias at a deployed model version' — and clearly identifies the two use cases: promoting a trained round into production or rolling back. It also distinguishes itself from related alias/GET tooling by noting this is the audited repoint operation and that it is feature-flag gated unlike the GET routes.

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 explicitly says when to use it: 'use it to promote a trained round into production or roll back to an earlier one.' It also gives a practical exclusion signal by noting the fineTuning flag off yields a 404 'unlike the GET routes,' helping an agent avoid attempting the call when feature-gated. It does not name exact sibling tools as alternatives, but the guidance is clear enough.

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