get_trained_model_id

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

No annotations exist, so the description carries the full behavioral burden. It usefully discloses a hard constraint about workflow states (an error/eligibility condition), but says nothing about what happens on failure, permission requirements, or output form.

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 compact sentences with zero filler; the core action is front-loaded and the constraint follows immediately. Nothing is redundant or padding.

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 simple one-parameter extraction tool with no output schema and no annotations, the description covers the action and the key eligibility constraint. The only gap is the absence of failure/error behavior detail, which is minor at this complexity.

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?

There is a single parameter with 100% schema description coverage, so the schema already documents 'workflow_id'. The description adds no format, source, or lookup detail beyond what the schema provides, making the baseline 3 appropriate.

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: 'Extract the trained model ID from a completed training workflow.' This clearly distinguishes it from siblings like list_models or get_model, which deal with model objects rather than the ID produced by a training workflow. It stops short of explicitly naming those siblings, so it is clear but not maximally differentiated.

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 supplies a precondition ('Only works on COMPLETED or FINALIZED workflows'), which tells the agent when the call will succeed. However, it never names an alternative tool or explains what to do when the workflow is not in one of those states, leaving usage to inference.

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