sunrise_monthly

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

Beyond the annotations (readOnlyHint, destructiveHint, etc.), the description reveals a key behavioral trait: if the current month has not ended, values are filled with predictions. It also discloses the return type as pandas.DataFrame, adding useful context.

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 includes the essential behavior, a source URL, and parameter documentation. It is slightly cluttered by the raw URL, but the overall structure is efficient and front-loaded with the core purpose.

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?

Despite lacking an output schema, the description sufficiently explains what the tool returns (a DataFrame of daily sunrise/sunset data for the month) and covers prediction behavior and parameter usage. This makes the tool fully comprehensible for an agent.

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?

With zero description coverage in the input schema, the docstring fully compensates by explaining both parameters: date is described as specifying the month with an example format '20200428', and city is described with input format examples like '北京'. This provides complete parameter semantics.

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 tool's function: returning daily sunrise/sunset data for the month containing the provided date, with predicted values for future days. This is specific and distinguishes it from the sibling tool sunrise_daily, which likely handles single-day data. The return type is also specified as a DataFrame.

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 implies the tool should be used when a full month of daily sunrise/sunset data is needed, using the date to select the month. It gives a clear context for use but does not explicitly mention alternatives or exclusions, so it stops short of a 5.

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