Agentic Fabrication Network (AFN)

Leave vendor review

leave_review

Record the user's verified-purchase review of how a UFP order turned out. YOUR JOB IS TO STRUCTURE THE FEEDBACK: turn what the user actually said into stars (1-5 overall) and facet scores — "the cut was sloppy" -> quality, "parts arrived 2 days late" -> timeliness, "wrong color" -> accuracy. Keep the optional comment to a short factual summary of their words; never embellish. ALWAYS draft the review (stars, facets, comment) and confirm it with the user BEFORE calling this tool — e.g. "I'll rate this 2 stars with timeliness 1 because it arrived 2 days late — send it?". One review per order: submitting again updates the existing review. Use list_orders first if you need to resolve which order they mean.

Input Schema

TableJSON Schema

Name	Required	Description
`stars`	Yes	Overall rating the user confirmed, 1-5
`claims`	No	CHECKABLE claims from the user's feedback — the network stamps each against its own records (the ordered spec, polled tracking + delivery events) and attaches evidence chips to the review. Only pass what the user actually asserted.
`facets`	No	Score only the facets the user actually spoke to
`comment`	No	Short factual summary of the user's feedback, in their spirit — optional
`order_id`	Yes	The UFP order being reviewed (ord_…) — see list_orders

TDQS

A4.7/5.0

Behavior5/5

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

The description discloses that the tool structures feedback into stars/facets, updates existing reviews on resubmission, and requires user confirmation. It also warns against embellishing comments. With no annotations provided, the description fully carries the burden of behavioral disclosure.

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 somewhat long but every sentence serves a purpose: purpose, structuring rule, confirmation requirement, update behavior, and reference to list_orders. It's dense but not wasteful, with no redundancy.

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?

For a tool with no annotations and no output schema, the description covers purpose, process, facet mapping, confirmation step, and update semantics. It adequately equips an agent to invoke it correctly, including handling ambiguous order references.

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?

The schema already provides 100% coverage with descriptions for all parameters. The description adds value by mapping user phrases to facet scores ('the cut was sloppy' -> quality) and clarifying comment brevity. This goes beyond the schema's parameter descriptions.

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 records a verified-purchase review for a UFP order, with a specific verb ('record') and resource ('review'). It distinguishes from siblings like list_orders or get_order_status by focusing on submitting feedback.

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?

It explicitly instructs to confirm the drafted review with the user before calling, and to use list_orders first to resolve which order. It also notes one review per order with update semantics. While it doesn't list alternatives or exclusions, these guidelines are clear and actionable.

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

A4.4/5.0

Disambiguation5/5

Each tool targets a distinct action in the fabrication workflow: quoting, refining, checkout, order status, reviews, etc. Even similar concepts like get_fabrication_quote and refine_quote are clearly separated by purpose (initial quote vs. modifying an existing one). Internal tools like poll_quote are explicitly marked and never confused with user-facing actions.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern in snake_case (create_checkout, get_fabrication_quote, refine_quote, etc.). The verbs are specific and predictable (create, get, list, refine, reply), making the API easy to navigate.

Tool Count5/5

With 10 tools, the server provides a focused but complete set for its purpose—ordering and managing physical fabrications. No tool seems superfluous, and each covers a necessary step in the user journey. This is within the ideal range of 3-15 tools.

Completeness4/5

The tool surface covers the full lifecycle: quoting, refining, checkout, order tracking, reviews, and vendor communication. Minor gaps exist (e.g., no explicit cancel or return flow), but these are likely handled outside the MCP or via the hosted platform. The main workflows are all supported without dead ends.

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