breakdown_m

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

The description adds substantial context beyond the readOnlyHint annotation: it details validation tiers, assumptions, pre-conditions, and failure modes, including explicit remedies. This helps the agent understand what the tool checks and what might invalidate its output. It does not contradict annotations and provides real behavioral insight into the tool's internal logic.

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 well-structured with clearly labeled sections (Validation, Assumptions, Pre-conditions, Failure modes, Alternatives, Typical N) and is front-loaded with the core purpose. While it is relatively long, every section contributes useful operational context, and there is no filler or repetition.

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?

For a tool with 10 parameters and an output schema, the description covers a great deal of necessary context: validation standards, assumptions, pre-conditions, failure modes, alternative estimators, and a minimum sample size. The only notable gap is the undefined term 'M', which could affect an agent's understanding of when this tool is applicable, but this is more a purpose-clarity issue than a completeness gap.

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?

The input schema already provides 100% coverage with descriptions for every parameter, so the baseline is 3. The description does not add any additional meaning to the parameters—for example, it does not explain what the 'method' variants (smoothness vs. others) imply for the breakdown calculation, nor does it clarify the role of 'e' (relative time period) in the computation.

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 states a clear action ('Compute the breakdown value of M') but never defines what M represents, leaving the agent without the key semantic context. It also does not differentiate this tool from its sibling 'breakdown_frontier' or other robustness/sensitivity tools, which could lead to mis-selection.

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?

Pre-conditions and failure modes imply when the tool is appropriate (it requires a DID result with an event study, never-treated comparison groups, pre-treatment periods, etc.), but the description does not explicitly state when to choose this tool over its named alternatives (sp.callaway_santanna, sp.did, sp.honest_did). Those alternatives are presented only as responses to specific failure modes, not as general guidance on tool selection.

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