Agentic Fabrication Network (AFN)

Get material guide

get_material_guide

Honest, cited material intelligence for material-choice / strength / durability / heat / outdoor questions ("my PLA part broke, what's stronger?", "will this survive outdoors?"). Returns caveats-FIRST facts: published specs (tensile, heat-deflection, UV/outdoor, chemical) each tagged vendor-cited vs general engineering knowledge, "stronger/tougher/hotter than X" ladders (PLA→PETG→ABS/ASA→nylon/PC→metals), and finish/coating options. Call it BEFORE answering such a question, then relay the facts. It supplies FACTS ONLY — comparative specs and vendor-admitted caveats, never a fitness-for-purpose or safety guarantee (that stays your judgment boundary); name express vendor claims "advertised", not "certified".

Input Schema

TableJSON Schema

Name	Required	Description
`process`	No	A UFP process to scope to ("fdm_print", "resin_print", "cnc", "sheetmetal", "laser_cut") — lists the materials that process makes
`material`	No	A material the user named or you are weighing ("PLA", "6061 aluminum", "stainless") — free-form; a family ask like "stainless" returns the whole family
`question`	No	The user's raw question — mined for intent (stronger/tougher, heat/hot, outdoor/UV) so the right ladder is surfaced

TDQS

A4.8/5.0

Behavior5/5

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

With no annotations, the description fully discloses behavior: returns caveats-first facts, tags sources (vendor-cited vs general), provides strength ladders, and explicitly states it never gives safety guarantees. This is comprehensive transparency.

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 detailed but well-structured, front-loading the core purpose. It is slightly long, but every sentence adds essential information. Could be tightened, but still efficient for the complexity.

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 no output schema, the description thoroughly explains what is returned. It covers all relevant aspects for a 3-parameter tool, including what the tool does and its limitations. Complete for its purpose.

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% with all parameters documented. The description adds value by explaining how 'question' is used to mine for intent, but does not significantly add beyond schema for 'process' and 'material'. Thus slightly above baseline.

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 provides material intelligence for specific questions (strength, durability, heat, outdoor). It lists concrete outputs like published specs, comparative ladders, and finish options. The purpose is distinct from sibling tools which handle orders, quotes, and reviews.

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?

The description explicitly instructs to call the tool BEFORE answering material questions and to relay facts. It sets boundaries by stating it supplies facts only, not fitness or safety guarantees. While it doesn't mention alternatives, sibling tools are unrelated, so no confusion.

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

All Agentic Fabrication Network (AFN) tools Inspect

Try in Browser

RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

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.

Resources

Need Help?