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

With no annotations, the description carries the behavioral transparency burden. It does disclose useful traits: the Taiwan score is site-compiled and meant as a neutral reference, includes sub-items, and the CNN portion includes its original timestamp. It does not, however, explain update cadence, data recency, derivation methodology, or any limitations.

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 short and front-loaded with specifics. The English closing sentence largely restates the Chinese sentence, adding mild redundancy, but overall the structure is efficient and scannable.

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 tool this is reasonably complete, listing both market sentiment components and noting sub-items and timestamps. However, there is no mention of output shape or update behavior, and the absence of annotations means the description alone must cover these expectations; it only partially does.

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 zero parameters and the schema is an empty object with 100% coverage, so there is nothing for the description to add. Baseline 4 applies for parameter-free tools.

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 resource: Taiwan stock market sentiment composite score (site-made neutral reference with sub-items) and US CNN Fear & Greed with original timestamps. It adds concrete scope beyond the title and can be distinguished from siblings like taiwan_market_overview and business_climate by its explicit sentiment focus.

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 usage context is implied by the content described—an agent can infer this tool is for sentiment data rather than fundamentals or market overviews. However, there is no explicit guidance on when to prefer this tool over siblings, nor any exclusion or alternative routing.

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