Is the MCP server stateless?

Yes. Your MESHY_API_KEY is used per request and never persisted on the server side.

Does MCP cost the same as the REST API?

Yes — every MCP tool call maps to a single REST call and consumes credits at the exact same rate. See the Pricing page for the full matrix.

What are the rate limits?

The MCP server shares the same rate-limit plane as the REST API. See the Rate Limits page for per-tier limits.

What Meshy data can the MCP access?

Only what the MESHY_API_KEY can access via REST. Scopes and permissions are identical.

How do I report issues?

File an issue at the meshy-mcp-server repository: https://github.com/meshy-dev/meshy-mcp-server/issues

AI Integration

Meshy exposes two channels for AI agents and coding assistants: the Meshy MCP server for tool-calling, and llms.txt / llms-full.txt for docs ingestion.

If you are integrating Meshy from Claude Code, Cursor, Windsurf, Codex, or any other MCP-compatible tool, install the MCP server below. If you are integrating from a plain chat agent with no MCP support, point it at https://docs.meshy.ai/llms.txt instead.

What is MCP

The Model Context Protocol (MCP) is an open standard that lets AI assistants call external tools and retrieve structured context. The Meshy MCP server wraps the Meshy REST API as a set of tools your agent can invoke — generate a model, check a task, download the result — without hand-writing HTTP code.

The MCP server is open source and published on npm as @meshy-ai/meshy-mcp-server. Source: github.com/meshy-dev/meshy-mcp-server.

You need a Meshy API key. Create one on the API Keys page in the Developer Platform. The MCP server reads it from the MESHY_API_KEY environment variable.

Quick Install

# Detects installed AI clients and configures Meshy for each
npx add-mcp @meshy-ai/meshy-mcp-server --env MESHY_API_KEY=msy_YOUR_API_KEY

Prefer a drop-in skill instead? The open-source meshy-3d-agent skill pack ships pre-written Meshy workflows (generate → poll → download) for Claude Code, Cursor, and OpenClaw. It calls the Meshy REST API directly — no MCP server required.

npx skills add meshy-dev/meshy-3d-agent

Available Tools

The MCP server exposes the Meshy REST API as tools, grouped by capability.

3D Generation

meshy_text_to_3d: create an untextured preview mesh from a text prompt
meshy_image_to_3d: create a 3D model from one image
meshy_multi_image_to_3d: create a 3D model from multiple images of the same object
meshy_text_to_3d_refine: add texture to a preview mesh

Post-Processing

meshy_remesh: change topology and/or polycount of an existing model
meshy_retexture: apply a new texture to an existing model
meshy_rig: add a skeleton to a 3D humanoid character
meshy_animate: apply an animation to a rigged character
meshy_convert: convert a model to another file format
meshy_resize: resize a model to real-world dimensions
meshy_uv_unwrap: generate a new UV layout for a model

Creative Lab

meshy_creative_lab: turn a photo into a finished Creative Lab product (runs the prototype and build tasks)

Image Generation

meshy_text_to_image: 2D image from text
meshy_image_to_image: 2D image from a reference image

Task Management

meshy_get_task_status: check task status and download URLs
meshy_list_tasks: list recent tasks, optionally filtered by type/status
meshy_cancel_task: delete a task. A PENDING task is removed and refunded; an IN_PROGRESS task cannot be canceled (the API returns 409); a finished task is deleted.
meshy_download_model: fetch a completed model file and save locally

Workspace

meshy_list_models: list your recent Text to 3D models (use meshy_list_tasks for other task types)

3D Printing

meshy_send_to_slicer: detect installed slicers and return a command to open the model in one (runs locally; no Meshy API call)
meshy_analyze_printability: free automated FDM printability check (watertightness, volume, holes, non-manifold edges, degenerate faces)
meshy_process_multicolor: convert a textured model into a multi-color 3MF file for printing
meshy_repair_printability: repair a model for FDM printing

Account

meshy_check_balance: query remaining credits

Tool names and behaviors may evolve. The authoritative list lives in the MCP server source.

Example Prompts

Drop these into your MCP-enabled chat as a starting point.

Generate a 3D fox from the prompt "a cartoon fox sitting", preview it, then
texture it with PBR maps. Download the final GLB to ./outputs.

Take the image at https://example.com/sculpture.jpg and convert it into a
riggable 3D character. Use `should_remesh: true` and 50k target polycount.

What's my current Meshy credit balance?

List my last 10 successful text-to-3d tasks and download the top 3 as GLB
into ./downloads/.

llms.txt and llms-full.txt

If your agent doesn't support MCP, or you want to ingest the Meshy docs into a prompt directly, point it at our plain-text surfaces:

llms.txt — compact index + integration instructions (the correct async-polling pattern, auth rules, rate limits, model choice, common mistakes).
llms-full.txt — every API page concatenated in a single file for single-fetch ingestion.
Per-page Markdown: append .md to any endpoint URL. Example: https://docs.meshy.ai/api/text-to-3d.md.

All three are regenerated every time the docs site builds, so they never drift from the HTML docs.

FAQ

Is the MCP server stateless?: Yes. Your MESHY_API_KEY is used per request and never persisted on the server side.
Does MCP cost the same as the REST API?: Yes — every MCP tool call maps to a single REST call and consumes credits at the exact same rate. See the Pricing page for the full matrix.
What are the rate limits?: The MCP server shares the same rate-limit plane as the REST API. See the Rate Limits page for per-tier limits.
What Meshy data can the MCP access?: Only what the MESHY_API_KEY can access via REST. Scopes and permissions are identical.
How do I report issues?: File an issue at the meshy-mcp-server repository: https://github.com/meshy-dev/meshy-mcp-server/issues