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

No annotations exist, so the description carries the burden, and it delivers: id casing convention with concrete examples, the failure mode for bad ids, and the fact that deprecated models are excluded from results. It omits nothing critical for a read-only listing, though auth/rate-limit context is absent.

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?

Dense but front-loaded: purpose and authority claim come first, then id format, then failure mode, then return shape. Some sentences run long and the trailing "provider: a provider id" clause reads as an appended afterthought rather than integrated guidance.

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?

No output schema exists, and the description supplies the return shape ({ id, label, features }) plus the id format and deprecation behavior. For a one-optional-parameter lookup tool, nothing an agent needs to call it correctly 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?

Schema coverage is 0% and the single parameter is undocumented in the schema, so the description must compensate — and it does, explaining that provider filters to only that provider's models. It also defines the expected id format, which the bare string type does not convey.

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?

States a specific verb and resource ("List AI providers and their current models") and adds a distinguishing scope claim: it is "the only source of valid aiProvider / aiModel values." That separates it cleanly from sibling catalog tools such as list_block_types and list_condition_operators.

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?

Explicitly tells the agent when to call it (before setting aiProvider/aiModel in ai-agent, ai-router, condition aiQuestion blocks) and why skipping it is risky ("a wrong id fails at save or run"). It also flags the exception case: configure_block accepts any string, so validation only bites later.

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