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

The description adds substantial behavioral detail beyond the annotations: carton rounding, MOQ enforcement, vendor minimums, landed-cost calculation, margin-erosion alerts, and OTB rationing. Safety annotations already establish it as a read-only, idempotent operation; the description complements this by explaining what rules the generated output enforces, though it could more explicitly state whether emails are actually sent or only formatted.

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 dense and front-loads the core transformation before listing enforcement rules and output formats. It is appropriately sized for a complex planning tool, though the single long sentence structure makes it slightly harder to scan.

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, 11 parameters, rich nested schema, and no output schema, the description does a good job explaining outputs, constraint handling, and optional live/demo modes. The main remaining gap is workflow positioning relative to the sibling replenishment-planning tool.

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 description coverage is already 100%, so the schema carries the parameter-documentation burden. The description highlights key semantics for max_cash_budget_usd and supplier-level rules, but most parameter meaning comes from the schema rather than the description.

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 specific verb and resource: it turns SKU demand signals into vendor-grouped purchase orders and names the exact CSV/email outputs. It is clear what the tool produces, but it does not explicitly differentiate itself from the sibling calculate_replenishment_plan, which likely handles the upstream planning step.

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?

Usage is implied by the detailed feature list and output formats, making it evident this is for PO generation rather than reconciliation or migration. However, there is no explicit 'use this when' guidance, no exclusions, and no direct comparison to calculate_replenishment_plan or reconcile_po_three_way_match.

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

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

Annotations already declare read-only, idempotent, non-destructive behavior, so safety needs no restating. The description still adds real value beyond them: it enumerates the computed outputs, discloses the 25-SKU sandbox cap versus unlimited on Pro/License, and flags that live sync requires a configured Shopify Admin connection. It does not mention whether results are persisted or purely computed, but the readOnlyHint covers the risk profile.

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 opening sentence front-loads purpose and the remainder packs the output list, the formula, and quota constraints with little wasted prose. It is dense but every clause earns its place; the only soft spot is the jump from formulas to pricing tiers mid-paragraph.

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?

There is no output schema, so the description's output enumeration is essential and it delivers that well, along with tier limits. What is missing is the input-mode story: an agent is not told that data can arrive via the skus array, a raw CSV, or live Shopify, or how those interact. For a tool with 8 optional top-level parameters and three mutually alternative data sources, that gap is material.

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 description coverage is 100%, so the schema already carries the parameter burden (service_level Z-scores, stockout_days, case_pack, supplier_rules overrides). The description adds no parameter-level guidance of its own beyond the safety-stock formula and the tier limit. Baseline 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 names a specific verb and resource (computes replenishment plan / reorder points) and enumerates the concrete outputs: ABC classes, stockout-adjusted velocity, safety stock, reorder points, inventory position, days of cover, carton-rounded recommendations. It is unmistakably distinct in substance from build_supplier_purchase_orders or reconcile_po_three_way_match, though it never names a sibling to draw the boundary explicitly.

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?

Implied usage is clear (forecasting/replenishment planning), and the free-tier ceiling of 25 SKUs per call plus the Pro/License requirement for live Shopify sync is useful call-time context. However, nothing says when to use this tool versus build_supplier_purchase_orders (the downstream PO builder) or migrate_stocky_csv_snapshot, nor how to choose among the three input modes (skus, inventory_csv, use_live_shopify).

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

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

Annotations already cover safety (readOnly, idempotent, non-destructive, closed-world), so the description focuses on adding value: it discloses that outputs are inferred/derived (lead times computed from Created-to-Received timestamps, pack sizes 'GCD-inferred', min spend 'historical') rather than authoritative, which is important caveat context. It stops short of stating how missing or malformed data is handled.

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?

A single dense sentence that front-loads the motivating problem (Stocky shutdown, no Supplier Directory export) before the payload. Every clause carries distinct information about an output artifact; slightly long, but nothing is filler.

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?

Two optional parameters, full schema coverage, and rich annotations mean little else is needed. With no output schema, the description does the heavy lifting by enumerating the reconstructed fields and the ready-to-use supplier_rules JSON, which is exactly what an agent needs to decide whether to call it and how to chain it.

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%, so the baseline is 3; the description adds the non-obvious semantic that the two parameters are alternatives ('or pass demo_mode: true'), i.e. either a real CSV or the fixture, which the schema alone doesn't convey. It does not clarify the 5MB limit or what a partial/truncated CSV yields, keeping it off a 5.

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?

Specific verb+resource: 'reconstructs every supplier profile' from a Stocky PO CSV, and it enumerates exactly which derived values (lead-time mean/stdev, latest unit cost, GCD carton packs, min PO spend, supplier_rules). It differentiates from the closest sibling by naming build_supplier_purchase_orders as the downstream consumer, though calculate_replenishment_plan and reconcile_po_three_way_match are never positioned relative to it.

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?

Clear context for invocation: paste a historical Stocky PO CSV, or use demo_mode to test against the built-in fixture. The Stocky shutdown framing makes the 'when' obvious. No explicit exclusions or error-condition guidance (e.g. malformed CSV, oversized file), but the trigger condition is unambiguous.

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

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

Annotations already declare readOnly/idempotent/non-destructive, so the bar is lower, yet the description still adds real behavior: determinism, the four specific mismatch classes detected, the two CSV export targets, the debit-memo artifact, and the 15-line free-tier cap. One ambiguity: 'Emits ... a vendor Debit Memo email' could read as an outbound send under readOnlyHint=true, though 'Emits' most plausibly means generated as output — worth clarifying but not a hard contradiction.

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?

Four sentences, front-loaded with the engine's purpose and backed by concrete audit targets and outputs; the paywall/quota sentence sits last where it belongs. Slight marketing padding ('Deterministic Finance Ops', 'silent unit-price creep') costs a point but nothing is buried.

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 14 parameters but 100% schema description coverage and no output schema, the description carries its weight by describing exactly what is returned (QuickBooks and Xero bill CSVs with verified quantities/costs, plus a debit memo) and the quota limit. It leaves demo_mode, date defaults, and failure behavior to the schema, which is acceptable but not 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?

Schema coverage is 100%, so the baseline is 3, and the description goes beyond it by tying business concepts to specific parameters — damaged vs good receiving units, freight cap overages (agreed_freight_cap_usd), unapproved surcharges (invoice_extra_fees_usd), and dual accounting exports. It still doesn't explain defaults like demo_mode or the date/currency conventions, so it improves on the schema without fully replacing it.

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 chain and the exact three data sources it reconciles (approved PO lines, dock receiving counts split good/damaged, billed invoice lines), plus the artifacts produced. This is clearly distinguishable from the sibling tools (replenishment planning, PO building, CSV migration), so an agent can route here for audit work without opening any schema.

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?

Usage context is implied by 'for wholesalers and Shopify merchants' and by naming the audit scenarios (price creep, short-shipments, surcharges, freight caps), but there is no explicit 'use this when / not when' statement and no reference to the sibling tools as alternatives. The only explicit condition is a licensing/quota note, which is not selection guidance.

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