train_risk_model

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

With no annotations provided, the description fully explains the tool's behavior: it trains a GradientBoostingClassifier, uses training history, reports metrics, and includes a caveat for small datasets. The disclosure about statistical meaningfulness is valuable. However, it doesn't mention how long training might take, whether it requires a running database, or if it's a destructive operation (overwrites the current model?), which would justify a 5.

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 three well-structured paragraphs: first a one-liner summary, then technical detail, then returns section. Every sentence adds value without redundancy. It is concise (every sentence earns its place) and front-loaded with the core purpose.

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 output schema is present, so return values are documented elsewhere. The description covers the algorithm, training data source, metric reporting with caveats, and honesty about statistical significance. This is complete for a training-tool description given the schema availability and zero parameters.

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 are zero parameters, and schema description coverage is 100% (schema is empty). The description doesn't need to explain parameters, but it adds value by detailing the training process and metrics returned. Baseline is 4 due to no parameters.

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 action ('Retrain the PR-risk classifier on everything currently in the database') and specifies the algorithm (GradientBoostingClassifier) and output metrics. It distinguishes the tool from siblings like 'predict_pr_risk' (which presumably uses the model, not trains it) and 'analyze_coverage' (which analyzes coverage, not risk).

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 implies the tool is for retraining the model using historical data, with no explicit when-to-use or when-not-to-use guidance. However, given that all sibling tools have distinct purposes (e.g., 'predict_pr_risk' for predictions, 'analyze_coverage' for coverage), the context is clear enough. No alternatives or exclusions are stated, so score 4.

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