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

With no annotations, the description carries the full burden. It discloses the output composition (performance summaries, benchmark comparison), the scope (public strategies, Taiwan stocks), and explicitly states what it does not provide (full content/code), which is strong behavioral context for a simple list operation.

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 sentences, no wasted words; the main purpose is front-loaded and the follow-up instruction is a single clear conditional sentence.

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 zero-parameter list tool with no output schema, the description sufficiently explains what is returned and how to proceed for more detail. Minor omissions like pagination or ordering are not critical given the minimal 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?

The tool has 0 parameters, so the baseline is 4. The description does not need to explain parameters; it focuses on output content.

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 action (list), the resource (FinLab public strategy library of Taiwan stock quantitative strategies), and the content of the list (backtest performance summaries with CAGR, Sharpe, max drawdown, and benchmark comparison). It also distinguishes itself from get_strategy by stating that full content/code requires that sibling.

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 explicitly routes to get_strategy for full strategy content and Python code, providing a concrete when-to-use-alternative condition. However, it does not explicitly state when this tool should be preferred over other siblings, leaving the main use case implied.

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