compute_betas

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

Annotations (readOnlyHint=true, openWorldHint=true) already indicate a safe, non-destructive operation. The description adds valuable behavioral context by listing return fields (R², rolling_window, factor_last_date, data_truncated_by, simulation_batch_id) and explaining the meaning of data_truncated_by as stale factors. It doesn't address potential edge cases, but the annotations cover the most critical behavior.

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 moderately long but tightly packed with useful information. Each sentence contributes either usage guidance, return value semantics, or analytical tips. It's not overly verbose given the tool's complexity.

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 description covers the tool's context comprehensively: prerequisites (trained model_group_id), relationship to other tools (analyze_quantitative, compute_returns), key use cases, and interpretation of outputs. Has output schema present, so return values don't need full explanation, but important fields are highlighted.

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 100%, so parameters are already documented. The description adds extra meaning for lookback_days by suggesting example values (63, 126, 252) and explaining their analytical purpose (identifying regime changes). This exceeds the schema's basic type/description info.

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 'Compute factor exposures (betas) for an already-trained model group' with a specific verb and resource. It distinguishes itself by noting when it's needed vs. when it's not (analyze_quantitative already includes this step).

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?

It explicitly states when to use this tool: when you have a trained model_group_id and want to refresh betas with a different lookback window or get a new simulation_batch_id. It also tells you when NOT to use it (after analyze_quantitative).

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