Moltline Dropship Economics

Lead Time

lead_time

Read-onlyIdempotent

Turn supplier and transit times into a date you can promise. FREE.

Adds a buffer, because the number a supplier quotes is their best case and the date you publish is a commitment. Typical input {"supplier_days": 3, "transit_days": 12, "order_date": "2026-08-20"} returns {"total_days": 17, "delivery_date": "2026-09-06", "promise_date": "2026-09-09", "advice": "..."}.

Use when writing a shipping policy or answering "when will it arrive". Not for what the shipping costs — that is the shipping server. Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": ""} (for example {"error": "order_date must be YYYY-MM-DD, e.g. 2026-08-20"}). Every call is read-only and idempotent, so after correcting the input it is always safe to retry.

Input Schema

TableJSON Schema

Name	Required	Description
`order_date`	No	Start date as YYYY-MM-DD, e.g. "2026-08-20". Defaults to today when omitted.
`buffer_days`	No	Extra days added before the date you publish. Default 2.
`transit_days`	Yes	Days in transit from despatch to the customer.
`supplier_days`	Yes	Days from your order to the supplier despatching it.
`working_days_only`	No	When true, count supplier and transit days as Monday-Friday only. Default false.

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

TDQS

A4.8/5.0

Behavior5/5

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

Beyond the annotations (readOnly, idempotent), the description adds concrete behavioral context: never raises a protocol error, returns an error object with corrective guidance, and explicitly confirms that retrying is safe. This significantly exceeds what annotations already provide.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is longer than average but every sentence adds value (purpose, usage, example, error handling, safety). The 'FREE' note is unnecessary noise but doesn't obscure the core content. Format is front-loaded with purpose and example.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's moderate complexity (5 params, output schema), the description covers all key aspects: calculation logic, example output, error behavior, idempotency, and usage context. No significant gaps remain.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

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 a concrete example with typical inputs and outputs, clarifying the buffer concept and error format, which helps agents understand parameter relationships beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's function: turning supplier and transit times into a promise date, with a specific example. It distinguishes itself from siblings by explicitly saying it is not for shipping costs and pointing to a separate shipping server.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides explicit when-to-use guidance ('when writing a shipping policy or answering when will it arrive') and an explicit exclusion ('Not for what the shipping costs'), which clearly differentiates it from related tools.

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

All Moltline Dropship Economics tools Inspect

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TDQS

A4.5/5.0

Disambiguation5/5

Each tool targets a distinct economic decision: lead_time for delivery dates, margin_check for single-sale profitability, moq_planner for order quantity, price_ladder for price setting, returns_impact for return rate effects, and sku_map_check for SKU mapping validation. Cross-references explicitly point out which tool to use for adjacent concerns, eliminating ambiguity.

Naming Consistency4/5

All tool names follow a consistent noun-based pattern (lead_time, margin_check, moq_planner, price_ladder, returns_impact, sku_map_check) with descriptive suffixes. The naming is uniform and predictable, though the pattern is noun_verb rather than verb_noun, which is a minor deviation from the most common convention.

Tool Count5/5

Six tools is well-scoped for a dropship economics server. Each tool covers a distinct aspect of the domain (delivery, margin, ordering, pricing, returns, SKU mapping) without redundancy or bloat. The count is appropriate for the stated purpose.

Completeness4/5

The tool set covers the core economic decisions in dropshipping: delivery promises, margin analysis, order quantity planning, price laddering, returns impact, and SKU mapping validation. Minor gaps exist (e.g., no tool for supplier comparison or multi-product portfolio analysis), but the surface is largely complete for the stated domain.

Resources

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