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

The description discloses important behavior beyond the minimal annotations: it hits the live decision engine, persists a new snapshot, and computes a diff against the previous scan. These side effects are relevant to an agent deciding whether to invoke the tool. It does not mention rate limits or authorization, but the provided behavioral detail is strong.

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?

Three dense sentences front-load the action, then list return content, then state persistence and use case. Every clause earns its place; there is no filler or redundancy.

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 one well-documented parameter, an output schema, and clear behavioral side effects, the description gives everything an agent needs: what it does, what it returns, what it persists, and when to use it. Nothing essential is missing.

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 schema already documents the only parameter fully (cohort id to scan), so the baseline is 3. The description adds the phrase 'saved cohort,' which reinforces the parameter's meaning, but it does not provide additional format or usage detail beyond the schema.

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 names a specific verb and resource: re-scan each member of a saved cohort against the live decision engine. It also distinguishes the tool by its outputs (triage list, diff vs previous scan) and side effect (persists snapshot), making it clearly differentiable from siblings like scan_savings.

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 final sentence gives an explicit use case: refresh a book of business and surface what changed. It does not name alternatives or state when not to use it, so it misses the upper bound, but the context is still clear enough for an agent to select it appropriately.

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