sentinel_attribute

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

With no annotations, the description carries full burden. It discloses internal steps (counterfactual construction, permutation p-value, surface events) and an output condition (low_confidence flag), but does not mention side effects, authentication needs, or rate limits. The algorithm is moderately well described.

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 two sentences with no extraneous information; it front-loads the core purpose and efficiently summarizes method and output. Could be slightly more readable for non-specialists, but overall concise.

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 (causal inference with multiple outputs) and no output schema, the description omits the expected return structure beyond the low_confidence flag. An agent would not know what fields (effect size, p-value, events list) to expect. This is a significant 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?

Schema coverage is 100% with both parameters described (ISO country code, date format). The description adds no further details about the parameters themselves, so the baseline of 3 is appropriate.

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 specifies a precise method (synthetic difference-in-differences for shutdown attribution), names the referenced methodology (Arkhangelsky et al. 2019, ISOC NetLoss 2024), and distinguishes this tool from sibling sentinel tools by focusing on causal inference for shutdown events rather than active learning or other analyses.

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?

No explicit usage guidance is provided; the description does not state when to use this tool over alternatives like atlas_incident_counterfactual or other sentinel tools, nor does it mention prerequisites or when not to use it.

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