ff_factors

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

No annotations are provided, so the description carries the full burden of behavioral disclosure, yet it only signals dataset freshness ('最新数据') and the dataset variant name. It does not disclose update frequency, which factor set is included (3-factor vs 5-factor), market coverage, or any caveats — leaving the agent to discover these only after invocation.

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?

A single front-loaded sentence packs the resource name, dataset variant, and content detail with no filler. It earns 4 rather than 5 only because the bilingual parenthetical ('Current Research Returns') is slightly awkward and the qualifiers could have been merged more cleanly.

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?

With zero parameters, an output schema present, and a clearly named dataset, invocation is straightforward and the essentials for selection are covered. It is incomplete on scope: factor set version, update cadence, and market coverage are absent, which could mislead an agent about what the returned data actually represents.

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 an empty schema, so the description is not required to elaborate on inputs. Its '最新数据' wording is consistent with a no-parameter fetch that simply returns the latest observation, matching the baseline of 4 for parameterless 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 names an unambiguous resource — the Fama-French multi-factor model's latest data ('Current Research Returns') — and adds a content qualifier ('含 Size 组合回报'). No sibling tool references factor-model data, so it is readily distinguishable from the large tool list. The fetch verb is only implied, which keeps it from a 5.

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 is purely content-focused and offers no when-to-use guidance, no exclusions, and no named alternatives. Apart from what the tool's own name implies, an agent gets no help deciding between ff_factors and other market-data or macro-data siblings.

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