Agentic Fabrication Network (AFN)

Reply to the maker

reply_to_maker

Send the user's answer to a question the maker (vendor) asked about a UFP order — get_order_status surfaces an open question when one is waiting. Confirm the answer with the user before sending. The network relays it: the maker sees plain text from the network account with the user's contact details and links stripped, so never include emails, phone numbers, or URLs the answer depends on.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`message`	Yes	The user's answer, in their words — it is relayed to the maker
`order_id`	Yes	The UFP order the maker asked about (ord_…)

TDQS

A4.7/5.0

Behavior5/5

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

With no annotations, the description carries the full burden. It discloses that the message is relayed via the network account, that contact details and links are stripped, and warns against including them. This is significant behavioral context beyond a simple 'send'.

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?

Two sentences, front-loaded with purpose, then usage guidance. Every clause adds value: the flow reference, the confirmation step, and the network sanitization warning. No wasted words.

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 absence of output schema, the description still provides enough context: when to use, how to use, and a critical behavioral caveat. It is complete for a two-parameter send action with strong usage guidelines.

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

Parameters3/5

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

Schema coverage is 100%, so the schema already documents both parameters. The description reinforces that message is in the user's words and relayed, but adds little beyond what the schema provides.

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 sends the user's answer to a maker's question about a UFP order. It references get_order_status, distinguishing this as the complementary action to surfacing the question.

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?

Explicitly instructs to confirm the answer with the user before sending and ties usage to get_order_status surfacing an open question. Also provides a clear exclusion: never include emails, phone numbers, or URLs the answer depends on.

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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