Train a custom model

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

With no annotations, the description carries the full behavioral burden. It usefully discloses that training is tracked asynchronously via the returned model id and that the resulting custom_model_uri feeds ideogram_generate, but omits cost, permissions, rate limits, and how to poll training status.

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 dense sentences, appropriately front-loaded with the purpose and constraints, then the follow-up tool chain. The 'hyperparameter routes to the advanced operation' phrase is the only bit of wasted/confusing text.

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 complex training tool with no annotations and no output schema, the description compensates by explaining the return value (model id) and downstream usage. Gaps remain around auth, cost, and async completion checking, but the call-and-follow workflow is complete enough to invoke correctly.

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 description coverage is 100%, so the schema already documents all ten parameters (batch_size, lora_rank, learning_rate, etc.). The description adds only the dataset size range, which is already in the schema, so it earns the baseline 3.

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 ('Start training a custom Ideogram 4.0 (default) or 3.0 model from a dataset of 15-100 images'), which is easy to distinguish from generation/edit siblings. The clause 'a hyperparameter routes to the advanced operation' introduces minor ambiguity, but the core purpose is unmistakable.

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?

Usage is only implied through the workflow chain ('follow with ideogram_models', 'generates through ideogram_generate') and the 15-100 image dataset constraint. There is no explicit when-to-use or when-not-to-use statement, and the 'advanced operation' routing is left unexplained, so the agent must infer conditions.

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