train_ml_predictor

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

With no annotations provided, the description bears full responsibility for disclosing behavioral traits. It merely says 'train' without explaining data dependencies, training duration, model persistence, side effects, or required permissions. This is insufficient for an agent to safely invoke the tool.

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 a single short sentence, which is concise but lacks structure. It could include more information without becoming verbose, such as a brief statement about output or prerequisites. It is adequate but minimal.

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?

The tool has 9 parameters, a complex training operation, and an output schema, yet the description gives no context about the output, training data requirements, model behavior, or how to use the results. This is severely incomplete for an agent to understand the tool's full usage.

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?

The input schema has 67% parameter description coverage (6 out of 9 parameters described). The description adds no additional meaning beyond stating the overall action. It does not explain the purpose of any parameter or how they influence training. For the 3 undocumented parameters (n_estimators, max_depth, min_samples_split), the description offers no compensation.

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 states the tool trains an ML predictor model for trading signals. It uses a specific verb ('train') and resource ('ML predictor model'), and distinguishes itself from sibling tools like 'run_ml_strategy_backtest' which focuses on backtesting the trained model.

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 no guidance on when to use this tool versus alternatives. There is no mention of prerequisites, when not to use it, or comparison to similar tools like 'run_ml_strategy_backtest'.

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