Skip to main content
Glama

recommend_material

Translates natural language intent and printer capabilities into an optimal 3D printing material recommendation with settings. Considers enclosure, heated bed, budget, and on-hand inventory.

Instructions

Recommend material from intent + printer capabilities (considers enclosure, bed, budget).

        Uses printer DNA + historical data to translate natural language
        intent (e.g. ``"make it strong"``, ``"make it pretty"``,
        ``"make it cheap"``) into an optimal material recommendation
        with settings.

        Pass ``printer_id`` to answer for a SPECIFIC machine — essential
        on a mixed fleet, where "what should I run this in" depends on
        which printer will run it.  The recommendation is then computed
        against that machine's nozzle state (abrasive materials on a
        brass nozzle get an explicit wear warning) and the response
        names the machine it answered for.

        Pass ``on_hand_only=True`` to recommend only from materials you
        physically have — recorded spools (``add_spool``) plus what's
        loaded on your machines (AMS/CFS sync).  Scoped to one printer
        this works on every tier; sweeping a multi-machine fleet in one
        call is a Kiln Business feature (https://kiln3d.com/pricing).  The recommendation's
        ``availability`` block then says WHERE the material is: which
        machine has it loaded, or that it's on the shelf and needs a
        spool swap first.  With ``printer_id`` the loaded half is
        scoped to that one machine (shelf spools always count — they
        can be swapped in); without it, every machine's load counts.
        When nothing on hand suits the request, the response returns
        the best catalog pick clearly labeled needs-purchase — it
        never silently widens to the catalog.  And when what you have
        works but a material you DON'T own fits the job materially
        better, the answer names that too, so "best of what you have"
        is never mistaken for "right for the job".

        **Which material tool to use:**

        - Quick intent-based pick for your own printer? → ``recommend_material`` (this tool)
        - Only from spools I actually own? → ``recommend_material(on_hand_only=True)``
        - Designing a part and need engineering specs? → ``recommend_design_material``
        - Ordering a print from a service? → ``suggest_material_for_order``
        - Which of MY printers has a material loaded? → ``find_printers_with_material``
        - What's loaded across the fleet right now? → ``get_fleet_material_summary``

        Args:
            intent: User intent text (e.g. ``"strong"``, ``"pretty"``).
            has_enclosure: Whether the printer has an enclosure.
            has_heated_bed: Whether the printer has a heated bed.
            budget_usd: Optional maximum budget per kg in USD.
            printer_id: Optional registered printer to answer for.
                Empty = printer-agnostic recommendation.
            on_hand_only: Restrict candidates to recorded inventory
                (loaded materials + shelf spools).  Default False =
                full catalog.
        

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
intentYes
budget_usdNo
printer_idNo
on_hand_onlyNo
has_enclosureNo
has_heated_bedNo
Behavior5/5

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

With no annotations, the description fully carries the burden, disclosing key behaviors: printer DNA usage, wear warnings, availability scoping, needs-purchase labeling, and naming better materials not owned. No contradictions.

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 thorough but somewhat lengthy. However, it is well-structured with clear sections (summary, details, tool comparison, Args) and every sentence adds value. Minor conciseness improvement possible.

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 6 parameters, no output schema, and 6+ sibling tools, the description covers all necessary context: inputs, behavior, edge cases (needs-purchase, best-of-have vs best-for-job), and ties to pricing tiers.

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 0%, but the description compensates by explaining all six parameters in detail with examples and context (e.g., intent as natural language, printer_id essential on mixed fleet, on_hand_only for inventory restriction).

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 'Recommend material from intent + printer capabilities' and provides a dedicated section listing sibling tools with usage scenarios, making the tool's purpose distinct and unambiguous.

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 includes a 'Which material tool to use:' section that explicitly differentiates this tool from six siblings with concrete when-to-use guidance. It also explains parameters like printer_id and on_hand_only in context of when they should be passed.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/codeofaxel/kiln'

If you have feedback or need assistance with the MCP directory API, please join our Discord server