research_stock_deep_dive

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

Beyond annotations (readOnlyHint, etc.), the description reveals the internal debate mechanism, the fact that Bull/Bear agents focus on supportive evidence only, the Synthesizer's broader view, and the cost per call. It also flags the Premium tier restriction, adding significant behavioral context.

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 a single paragraph that front-loads the main purpose, but it includes somewhat verbose details (emojis, internal process explanation) that could be tightened without losing clarity.

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 absence of an output schema, the description compensates by explaining the return structure (each agent's reasoning, action, confidence score). It also covers cost, tier restriction, and the internal architecture, providing a comprehensive understanding of the tool's behavior and output.

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?

All parameters have descriptions in the schema, so the description adds no extra parameter semantics beyond what the schema already provides. The baseline score 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 identifies the tool as an in-depth stock research tool using 5 AI agents in a parallel debate, culminating in a 6-level final recommendation. It distinguishes itself from sibling tools by specifying the multi-agent, debate-style analysis.

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 states the tool is more robust than single LLM analysis and mentions the Premium tier requirement, but does not explicitly mention when not to use it or suggest alternative tools for specific scenarios.

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