prob_forecast

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

No annotations provided, so description must disclose all behavioral traits. It mentions output includes downsampled rate profiles and cumulative milestones, but lacks details on error handling, input validation, or performance implications. Does not address what happens with invalid distributions or 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?

Concise at around 100 words. Structure is efficient: purpose statement, sibling comparison, output description, parameter list. Front-loaded with the most important information. No unnecessary sentences.

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 complexity of a probabilistic forecast tool and the presence of an output schema, the description is reasonably complete. It covers inputs and key output details. However, it could mention that results are returned in a structured format (e.g., table) and clarify the units of rate profiles.

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 coverage is 0%, but the description's Args block explains each parameter: qi_dist, di_dist, b_dist are described as dicts with 'mean' and 'std', and defaults for optional parameters. This adds significant meaning beyond the schema's type-only specification.

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?

States clearly that it generates P10/P50/P90 rate-time forecast profiles, distinguishing from mc_eur which returns only EUR summaries. The verb 'Generate' and resource 'rate-time forecast profiles' are specific and unambiguous.

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?

Explicitly contrasts with mc_eur, indicating when to use this tool (when full rate-time curves are needed instead of just EUR summaries). Does not provide exclusions or when-not-to-use scenarios, but the comparison gives clear context.

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