article_ff_crr

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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds that the return is a single pandas DataFrame, which is useful context about the output format. It does not add deeper behavioral details like pagination or content specifics, but given the strong annotated safety profile, this is acceptable.

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 brief, containing the title, a URL, and return type information. However, the raw URL is a large block that disrupts readability and does not directly support tool invocation. The structure is minimally acceptable but could be improved by placing the URL on a separate line or integrating it as a reference.

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 no output schema and no parameter schema, the description is the sole source of information about what the tool returns. It states 'FF多因子模型单一表格' and 'pandas.DataFrame' but does not explain what factors are included, the table's columns, or any other relevant details. The URL hints at the data source but does not sufficiently describe the actual output structure. This is incomplete for an agent to know what to expect.

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 is empty with zero parameters, so there are no parameter semantics to elaborate. Baseline for zero params is 4, and the description does not need to compensate for any missing parameter documentation. The description's mention of a single table and DataFrame provides some output context.

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 as the FF multi-factor model and states that it returns a single table, which implies a retrieval operation. The inclusion of the Ken French data library URL adds context. However, it does not explicitly differentiate from sibling tools or state a verb like 'get', so it is clear but not fully distinguishing.

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 provides no explicit guidance on when to use this tool over alternatives. It neither mentions exclusions nor suggests similar tools like article_epu_index. Usage is only implied by the name and the content, which is insufficient for an agent to decide between many article_* tools.

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