analyze_stock

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

No annotations are provided, and the description does not disclose output format, data sources, whether recommendations are returned, or any limitations. It only names analysis categories, leaving significant behavioral uncertainty for a tool with no structural annotations.

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, front-loaded declarative sentence with no filler or redundant restatement of the tool name. Every word contributes to conveying the tool's scope.

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?

No output schema exists, yet the description does not explain the return value or how the analysis is presented. It also does not mention preconditions or limitations, so overall invocation context is incomplete despite complete parameter documentation.

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 already provides descriptions for all five parameters, so the baseline is 3. The tool description adds no parameter-level meaning beyond what the schema already states.

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?

Description uses a specific verb 'analyze' with a clear resource 'stock' and enumerates the content areas (technical indicators, fundamentals, investment signals). This clearly differentiates the tool from sibling tools focused on specific strategies like Monte Carlo simulation or backtesting.

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?

No explicit guidance is provided about when to use this tool versus alternatives. Sibling tool names suggest distinct use cases, but the description does not name them or state exclusions, leaving the agent to infer applicability.

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