Explain ML prediction

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

Annotations already provide readOnlyHint, so safety is covered. The description adds behavioral context by mentioning signed magnitudes and directional effects on probability, which goes beyond annotations. It doesn't describe output format or edge cases, but the added context is meaningful.

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 concise sentences deliver core functionality and usage guidance with no unnecessary words. The description is front-loaded and easy to scan.

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 tool with three parameters and no output schema, the description covers purpose, usage context, and a key constraint. It hints at return content but lacks details on ordering or error behavior. Overall, it's sufficiently complete for an agent 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 coverage is low (33%), but the description compensates for the most ambiguous parameter (horizon) by requiring it to match the prediction. The top parameter is already described in the schema, and ticker is self-evident from the tool name. Partial compensation is sufficient.

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 explaining the top feature contributions behind an ML outperformance prediction, with signed magnitudes showing what pushed probability up/down. It distinguishes itself from sibling stoquant_get_ml_prediction by emphasizing the 'why' aspect.

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?

Explicitly states to use after stoquant_get_ml_prediction when the user asks why the model is bullish/bearish. It also provides a critical constraint that the horizon must match the prediction being explained, giving clear when-to-use guidance.

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