analyze_cherry_picking

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It only says the tool analyzes cherry-picking and returns a CherryPickingAnalysis dict; it does not reveal how the time series is processed, how dates are interpreted, whether the claimed variation is recalculated, or any edge cases.

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 well-structured, leading with the purpose and then listing parameters and return type. It avoids unnecessary prose, though some parameter descriptions are slightly redundant with the parameter names.

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?

Given five required parameters, nested objects, and no annotations, the description is not complete enough for an agent to invoke the tool confidently. The return value is mentioned only as a dict, and input formats for dates and time_series are not specified. The existence of an output schema helps, but input construction remains underspecified.

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 add basic meaning for each parameter, including the allowed values for claimed_direction ('hausse', 'baisse', 'stable') and the role of claimed_variation. However, it omits critical format details such as date string formats and the expected structure of the TimeSeries object.

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 opens with a clear, specific verb and resource: 'Analyze whether a claimed time-period variation is cherry-picked.' This is enough to distinguish it from general data retrieval siblings like eurostat_get_data or compare_values, though it does not explicitly name or contrast those alternatives.

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 siblings such as compare_values or score_check_worthiness. The description states what the tool does but gives no context about the expected analytical workflow or when cherry-picking analysis is appropriate.

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