movie_boxoffice_monthly

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

Annotations already declare readOnlyHint=true, destructiveHint=false, and idempotentHint=true, so the agent knows this is a safe read operation. The description adds the return type (pandas.DataFrame), which is useful but not a behavioral trait. It does not disclose any additional traits such as data freshness, pagination, or unique constraints. With annotations covering safety, this is adequate but not rich.

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 compact and structured: a title line, a source URL, and a docstring with param/return/type. Each element is relevant, though the URL and docstring could be seen as slightly redundant. Overall, it is appropriately sized with no fluff.

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, the description should explain what the returned DataFrame contains, but it only says '单月票房' (monthly box office) without specifying columns, scope, or data source details. It also lacks guidance on when to choose this tool over sibling movie box office tools. This is insufficient for a tool with no structured output documentation.

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 schema only specifies the 'date' parameter as a string with a default, but the description's docstring explains that the date determines the month for which box office data is returned. This adds meaningful semantics. However, the format (e.g., YYYYMMDD) is only implied by the default value '20240218', not explicitly stated, so it is not fully comprehensive.

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 monthly movie box office data and explains the date parameter's role in selecting the month. While it lacks an explicit verb like 'get' or 'list', the docstring's return statement makes the function's purpose apparent. It does not explicitly distinguish from sibling movie box office tools, but the 'monthly' qualifier is implicit.

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?

No guidance is provided on when to use this tool versus the many sibling tools (e.g., movie_boxoffice_daily, movie_boxoffice_weekly, movie_boxoffice_yearly). There are no conditions, exclusions, or alternative recommendations. The only hint is the name and the date parameter description, which is insufficient for an agent to select among closely related tools.

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