reference_class_estimate

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

Annotations provide readOnlyHint=true, idempotentHint=true, so the tool is safe and idempotent. Description adds value by explaining the estimation method (historical correction factors, industry averages) without contradicting annotations.

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?

Extremely concise: two paragraphs, four sentences total. Critical information is front-loaded, with no extraneous content.

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?

Explains the core estimation approach and usage context well. Lacks description of the output format, but given the tool's nature (estimates), the description is largely complete.

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?

Input schema covers 100% of parameters with descriptions. Description provides context about the estimation logic but does not add significant detail beyond the schema for individual parameters.

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?

Clearly states it provides data-driven estimates using reference class forecasting with historical correction factors. Distinguishes itself from siblings like cocomo_estimate and pert_estimate by emphasizing historical data prioritization.

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 tells when to use ('Prioritize this over algorithmic models when historical data is available') and implies when not to (use industry averages when no historical data). Lacks explicit naming of alternatives but provides solid guidance.

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