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

Annotations already declare a safe read-only, idempotent, non-destructive profile, but the description adds substantial context beyond them: pure calculation with no network request, nothing sent or stored, an x402 cost of $1 USDC per call via _meta, and that invalid input returns an error without charging. That is a rich behavioral picture an agent cannot infer from structured fields alone.

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?

Front-loaded with purpose followed by outputs, usage, and pricing; every section is load-bearing. The long enumeration of return metrics is justified because there is no output schema, though it could be tightened slightly.

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?

With no output schema, the description carries the full burden of describing returns and does so thoroughly — grade, score, aging buckets, overdue %, DSO, concentration, collection priority list with next action, and recommendations. Pricing and error behavior are also covered, leaving no material gap for correct invocation.

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 high (83%), so the schema already documents most parameters. The description still adds value by naming supported CSV sources, noting that columns are auto-detected, capping input at 1000 invoices, and clarifying the CSV/JSON row duality — meaning beyond the raw field list.

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 a specific verb and resource ('Grade the health of a business's accounts receivable from its invoice list') and names the source data (invoice list/CSV/JSON). It also distinguishes itself from siblings by pointing to payment_reminder_draft and late_fee_calc as downstream actions rather than duplicating them.

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?

'Use it for a monthly AR review or before chasing payments' gives explicit triggering contexts, and the description routes the agent to payment_reminder_draft and late_fee_calc for next actions. It stops short of stating when NOT to use it or how it differs from payment_terms_check/validate_invoice.

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