mu

Check spec / legal / supplier requirements for a make

mu_check

Ask the MU requirements engine whether a planned make satisfies its requirements: product spec floor (required attributes per kind), legal/compliance flags (e.g. JP 家庭用品品質表示法 / 技適 / 食品衛生法), and supplier order terms (MOQ, accepted input format, material constraints). Pass kind (required) + optional region (e.g. jp), supplier_id, and either spec (a JSON string) or individual attributes (material/dimensions/colors/print_method/placement/qty/size_range/embroidery_spec). Returns { ok, kind, report: { ok, gaps[], actions[] } }. Read-only, no API key. Use before mu_create_product or mu_rfq_create.

Input Schema

TableJSON Schema

Name	Required	Description
`kind`	Yes	Product kind (e.g. gi / tote / rashguard_ls).
`spec`	No	Spec as a JSON string, e.g. {"material":"8oz canvas","dimensions":"A4"}.
`colors`	No
`region`	No	Region, e.g. jp/us. Triggers region-specific legal requirements.
`material`	No
`placement`	No
`dimensions`	No
`size_range`	No
`supplier_id`	No	Supplier id (e.g. isami_gi) to also check its order terms.
`print_method`	No
`embroidery_spec`	No

TDQS

A4.5/5.0

Behavior4/5

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

With no annotations provided, the description carries full burden. It states the tool is read-only, requires no API key, and describes the return structure (ok, kind, report with ok, gaps, actions). Missing details on error handling but sufficient for most scenarios.

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

Conciseness5/5

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

The description is a single, well-structured paragraph of four sentences. It front-loads the core purpose, lists key parameters, gives return type, and ends with usage advice. No unnecessary words.

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

Completeness4/5

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

For a tool with 11 parameters and no output schema or annotations, the description covers the essential: purpose, inputs, output shape, and usage context. It lacks details on error handling or edge cases, but overall is complete enough for effective use.

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

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is low (36%), but the description compensates by explaining the purpose of each parameter, the relationship between spec JSON and individual attributes, and how region triggers legal checks. This adds significant value 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 checks spec, legal/compliance, and supplier requirements for a planned make. It distinguishes from siblings by explicitly naming mu_create_product and mu_rfq_create as tools to use after this check.

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

Usage Guidelines4/5

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

The description advises using this tool before mu_create_product or mu_rfq_create, provides usage context (read-only, no API key), but does not exhaustively list when not to use it or cover all sibling alternatives.

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

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TDQS

A4.1/5.0

Disambiguation5/5

Each tool has a clearly distinct purpose; no two tools appear to do the same thing. Even closely related tools (e.g., mu_quote vs mu_rfq_create, mu_preview_mockup vs mu_create_product) are differentiated by read-only vs. action, or draft vs. send.

Naming Consistency5/5

Tool names consistently use snake_case with the mu_ prefix, and follow a clear verb_noun or noun_verb pattern. Groups like mu_gi_*, mu_ship_*, mu_rfq_* maintain internal consistency. No arbitrary or ambiguous names.

Tool Count4/5

The tool count of 28 is on the higher side but appropriate for the breadth of functionality (registration, product lifecycle, manufacturing, shipping, sales, admin). It's well within a manageable range for a comprehensive server.

Completeness4/5

The tool surface covers core workflows comprehensively: registration, product CRUD (with create, update, retire), manufacturing quotes (informational and RFQ with spec drafting), shipping (CSV, tracking, status updates), sales data, and gi-specific management. Minor gaps include lack of store update/delete and product search, but these are not critical for the main use cases.

Resources

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