news_economic_baidu

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

Annotations already declare readOnlyHint=true and idempotentHint=true, so the description's added value is limited. It does add the source URL and the return type as a pd.DataFrame, which is useful. However, it does not disclose potential requirements like whether a cookie is mandatory for full data access or any pagination/rate-limit behavior, though the simple nature of the query lessens the need.

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 concise and front-loaded with the title and source, followed by parameters and return type. Every line serves a purpose, and there is no filler. The structure is clear and efficient.

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?

The tool has no output schema and minimal parameter descriptions, so the description must carry more weight. It fails to explain what 'economic data' actually includes, whether the data is from an economic calendar, or how to interpret the returned DataFrame. For an agent selecting between this and similar macro data tools, this is insufficiently complete.

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?

Schema description coverage is 0%, so the description must compensate. It does specify the date format (YYYYMMDD) and mentions the cookie parameter, but 'cookie' is just restated without explaining its purpose or when it's needed. The return type is noted, but column semantics are absent. This is adequate but minimal compensation.

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 (百度股市通-经济数据) and implies the action of retrieving economic data, with a URL and return type. It distinguishes itself from siblings by naming the Baidu Stock Calendar source, though it doesn't explicitly contrast with similar macro data tools. The purpose is understandable but could be more explicit about the specific nature of the data (e.g., calendar events).

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?

There is no guidance on when to use this tool versus the many macro_* or news_* siblings. The description only lists parameters and return type, leaving the agent to infer usage context. No exclusions, prerequisites, or alternative tool references are provided.

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