get_ai_prediction

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

With no annotations provided, the description fully carries the burden of behavioral disclosure. It details the prediction engine (gradient-boosted trees, LSTM, VQC) and features (VIX, relative strength, Treasury rates, options flow). It also notes per-ticker accuracy variation and describes all return keys. It does not mention any destructive side effects, which aligns with a read-only prediction tool. However, it could mention if results are cached or if there are rate limits.

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 well-structured with clear sections: main purpose, usage guidelines, engine details, parameters, and return values. It is front-loaded with the essential purpose. Every sentence adds value without redundancy. The technical depth is appropriate for an AI prediction tool.

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?

Given the tool's complexity and lack of an output schema, the description provides a thorough explanation of return values (symbol, prediction, up_probability, confidence, model_votes, regime, signal_strength). It also explains the model ensemble and input features. However, it does not detail how to interpret specific outputs like signal_strength or regime in practical terms, leaving some ambiguity for an AI agent.

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 provides only the parameter name 'symbol' with 0% description coverage. The description compensates excellently by explaining the symbol is an uppercase exchange ticker, providing examples ('NVDA', 'META', 'QQQ'), and adding valuable context about per-ticker model accuracy. This adds significant meaning beyond the schema.

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's core action: 'Get an AI/ML directional prediction for a stock's next-session move.' It uses a specific verb ('Get') and identifies the resource ('AI/ML directional prediction'). This clearly differentiates it from sibling tools like analyze_stock or get_iv_radar, which serve different purposes.

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 explicit 'Use this tool when' conditions such as needing a probability estimate, ensemble breakdown, or comparison with IV pricing. While it does not explicitly state when not to use it or mention alternatives, the context makes the appropriate use cases clear. The guidelines are practical but could be enhanced with explicit exclusions.

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