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Meshy MCP Server

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by meshy-dev

Generate 3D Model from Text

meshy_text_to_3d

Generate an untextured 3D model preview from a text prompt for approval, then add textures in a later refine step.

Instructions

Generate an untextured 3D PREVIEW from a text prompt; texture it afterwards with meshy_text_to_3d_refine once the user approves. Typically 2–3 minutes. Cost: 20 credits (meshy-7.1 / latest / meshy-6), 5 (meshy-6-lite, meshy-t2, meshy-t2.5); Ultra geometry +5.

PREFER THE IMAGE ROUTE: for higher quality and more control, generate a design image first (meshy_text_to_image), let the user approve it, then meshy_image_to_3d with input_task_id = the image task (textured in one task). Use direct text-to-3d for a quick draft or when the user explicitly asks for it.

Examples:

  • Low-poly: { prompt: "dragon", model_type: "smart-topology", ai_model: "meshy-t2", target_polycount: 4000 }

  • Max detail: { prompt: "ornate knight helmet", ai_model: "meshy-7.1", geometry_resolution: "4k" }

  • 3D print: { prompt: "cat", target_formats: ["obj"] }

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
promptYesWhat to model (max 800 characters)
ai_modelNo'latest' (default, = Meshy 7.1) or 'meshy-7.1' (best), 'meshy-6', 'meshy-6-lite' (fast, cheap, 2K textures only); 'meshy-7' is deprecated. With model_type smart-topology: 'meshy-t2' (the default there) or 'meshy-t2.5' (newest, sharper detail for game assets).
topologyNoquad or triangle (default).
auto_sizeNoAI-estimate the real-world height and resize to it (default false).
origin_atNo'bottom' or 'center' (default bottom with auto_size).
pose_modeNo'a-pose' or 't-pose'; use 't-pose' when the model will be rigged or animated.
model_typeNostandard (default); smart-topology (ai_model meshy-t2 or meshy-t2.5): a clean, part-separated, triangle-only mesh made directly at target_polycount, ignoring remesh/decimation; lowpoly is deprecated (billed like meshy-6, retires 2026-10-30).
ultra_modeNoDeprecated: use geometry_resolution '2k'.
should_remeshNoRemesh to target_polycount / topology (default false).
symmetry_modeNoDeprecated: has no effect.
target_formatsNoFormats to produce (default: all but 3mf; list '3mf' to get it). Fewer formats finish faster.
alpha_thumbnailNoAlso return a transparent-background preview (alpha_thumbnail_url).
decimation_modeNoAdaptive decimation 1 (ultra) – 4 (low); overrides target_polycount.
response_formatNomarkdown (default) or jsonmarkdown
target_polycountNoStandard: remesh target 100–300,000, needs should_remesh true. Smart Topology: meshy-t2 100–15,000 (default 4,000), meshy-t2.5 100–30,000 (default 15,000).
geometry_resolutionNoUltra geometry pass: standard (default), 2k or 4k (finest); meshy-7.1/latest with model_type standard only, +5 credits.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
statusYes
messageYes
task_idYes
estimated_timeYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.6.4

TDQS

A4.8/5.0
Behavior5/5

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

Annotations only cover the generic safety profile (not read-only, not idempotent, not destructive, open-world); the description adds the non-obvious operational traits: an untextured preview output that must be refined, a typical 2–3 minute runtime, and a per-model credit cost table including the +5 Ultra surcharge. That is exactly the latency/cost disclosure annotations cannot carry.

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?

Front-loaded with the core action, then the alternate route, then compact example objects; no filler sentences. It is on the longer side and the cost parenthetical is dense, but each block (workflow, routing, examples) carries distinct decision value.

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 16-parameter creation tool with an output schema present, the description covers the workflow position (preview -> refine), the competing route, cost/latency expectations, and worked parameter combinations. Return values are handled by the output schema, so nothing an agent needs to invoke this correctly is missing.

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 description coverage is 100%, so the baseline is 3. The description goes beyond it by mapping ai_model choices to credit costs and by giving three concrete parameter bundles (low-poly, max detail, 3D print) that demonstrate how enum values like model_type, ai_model, geometry_resolution and target_formats are meant to be combined.

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 states a precise verb+resource+qualifier: 'Generate an untextured 3D PREVIEW from a text prompt', and immediately names the follow-on sibling (meshy_text_to_3d_refine) and the contrasting sibling route (meshy_image_to_3d). An agent can distinguish this from every sibling without opening a schema.

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?

Explicit when-to-use and when-not: 'PREFER THE IMAGE ROUTE ... for higher quality and more control', with 'Use direct text-to-3d for a quick draft or when the user explicitly asks for it.' It also states the sequencing requirement (texture afterwards with refine once the user approves).

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