Invoke AI-as-a-Service OpenAPI

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

Annotations already declare destructiveHint=true and openWorldHint=true, so the safety profile is covered. The description adds genuinely useful behavioral facts beyond that: the 43-op allowlist, prefix-stripping normalization, and the explicit guarantee that unknown paths are rejected rather than invented. It does not say what a rejected call returns or whether destructive ops require confirmation, but the added constraints are substantive.

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?

Three dense sentences, front-loaded with the [OPENAPI] marker and the scope, then the path rule, then the allowlist/rejection guarantee. Telegraphic but every sentence carries distinct information; no filler.

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 destructive, open-world proxy with no output schema, the description covers scope, allowlist size, path handling, and rejection behavior, which is the core an agent needs. It is silent on the shape of error responses, how the 43 allowed operations map to the sibling tools, and any auth expectations, leaving gaps for a tool this broad.

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 only 25% across 4 params; only 'path' carries a schema description. The description partially compensates by explaining path normalization ('/ai/v1 prefix is stripped') and acceptable path forms, which is real meaning beyond the schema. But 'body' is an untyped empty schema and 'query'/'method' semantics get no treatment, so the coverage gap is not fully closed.

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 mechanism and resource: a passthrough OpenAPI proxy exposing 'Full ai-ml-backend 1.0 on /ai/v1' with a 43-op allowlist. An agent understands what the tool is. However, it does not distinguish itself from the many dedicated sibling tools (list_ai_endpoints, list_ai_models, get_ai_dataset) that cover the same AI domain, so an agent cannot tell from this text alone when the proxy is preferred over them.

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?

Gives operational conditions ('paths may be /endpoints or /ai/v1/datasets', 'prefix is stripped when present') that help an agent form a call, but offers no when-to-use guidance relative to the dedicated list_ai_*/get_ai_* siblings or the other invoke_* proxies. Usage is implied by the resource scope rather than stated.

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