analytics_52_week_forecast

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions 'advanced trend modeling and uncertainty analysis', hinting at computational complexity and probabilistic outputs, but fails to describe critical behaviors such as required permissions, rate limits, output format, error handling, or whether it modifies data. For a complex forecasting tool with zero annotation coverage, this is a significant gap.

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 a single, efficient sentence that front-loads the core purpose without unnecessary details. It avoids redundancy and wastes no words, making it easy to parse quickly. This is an example of optimal conciseness for a tool description.

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 the tool's complexity (4 parameters with nested objects, no output schema, and no annotations), the description is incomplete. It lacks parameter explanations, behavioral details, output information, and differentiation from siblings. While concise, it does not provide enough context for the agent to understand how to use the tool effectively in this rich environment.

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%, meaning no parameters are documented in the schema. The description does not mention any parameters or their semantics, leaving all 4 parameters (including complex nested objects like 'categories' and 'drivers') unexplained. This forces the agent to infer meaning from schema structure alone, which is insufficient for effective tool invocation.

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 states the tool's purpose: 'Generate 52-week rolling cash flow forecast with advanced trend modeling and uncertainty analysis'. It specifies the verb ('Generate'), resource ('52-week rolling cash flow forecast'), and key features ('advanced trend modeling and uncertainty analysis'). However, it does not explicitly differentiate from its sibling 'analytics_13_week_forecast' or other forecasting tools like 'cash_flow_forecast', which prevents a perfect score.

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 guidance on when to use this tool versus alternatives. With many sibling tools available (e.g., 'analytics_13_week_forecast', 'analytics_scenario_comparison', 'cash_flow_forecast'), there is no indication of specific use cases, prerequisites, or comparisons. This leaves the agent without direction on tool selection.

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