altair_train_classifier

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

No annotations are provided, so the description must carry the full burden. It states the return value (performance vector) but does not disclose side effects (e.g., whether the model is saved or if the training set is modified). This leaves some behavioral ambiguity.

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 concise with two sentences, each serving a distinct purpose: the first defines functionality, the second specifies return and scope. No wasted words.

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?

Given the absence of an output schema, the description adequately explains the return type (performance vector). It covers the main purpose and key parameters, though it could mention prerequisites (e.g., labelAttribute must exist in data) or outcome of training.

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 60%, and the description adds value by mapping the learner enum to human-readable model names and explaining the folds parameter in the context of k-fold cross-validation. It also clarifies the relationship between csvFile and repositoryEntry.

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 clearly identifies the tool as training and evaluating classification models with k-fold cross-validation, listing eight specific model types. This verb+resource combination is distinct from sibling tools like clustering or association rules.

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 provides context by mentioning the covered actions (46-55, 66-74) and supervised learning + validation, implying when to use. However, it lacks explicit exclusions or alternatives, such as mentioning when to use other classification-related sibling tools.

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