fahali_get_72h_forecast

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

With no annotations, the description carries full burden. It details the composition from multiple signals, horizon specificity (6-72h), the meaning of roles and tiers, and the separation of detection confidence from outcome probability. No behavioral traits are hidden or contradicted.

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 lengthy but front-loaded with key output components. It could be slightly more concise, but the complexity of the tool justifies the detail. Every sentence adds value, explaining nuances.

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?

Despite lacking an output schema, the description thoroughly explains the return structure, including the distinction between detector and forecaster roles, stance interpretation, and access requirements (Professional tier). It is complete for a tool of this complexity.

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 both parameters are described in the schema. The description adds minimal extra meaning beyond what the schema provides (e.g., confirms symbol is required and include for extras). Baseline of 3 is appropriate.

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 returns a probabilistic forecast for a symbol, listing components like crash/pump/neutral probabilities, expected return, uncertainty cone, driver contributions, and historical accuracy. This specific verb+resource distinguishes it from sibling tools like fahali_get_market_sentiment or fahali_get_highest_conviction_signals.

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 important context on how to interpret outputs (e.g., detectionConfidence vs outcomeProbability, stance field) and warns that most Fahali agents are detectors, not trade-signal generators. While it doesn't explicitly list when to use alternatives, the guidance on reading results is clear and helpful.

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