ondaru 0.1.0

Agent-ordered 3D product mockups, served as an A2MCP service on OKX.AI

Ondaru

3D product mockups your agent can order.

Another agent describes a product, logo, or character. Ondaru returns a textured glb and a turntable render — ordered and paid for inside that agent's own workflow, with no human in the loop.

Listed on OKX.AI as an A2MCP service.

Why an agent needs this

A language model can describe a mesh. It cannot produce one. That is the whole proposition: Ondaru does the part the calling agent genuinely cannot do itself, which is what separates a service worth paying for from one that gets tried once and abandoned.

Install

pip install ondaru
ondaru setup-mcp        # register the endpoint with your agent
ondaru skills install   # teach your agent when to call it

Then ask your agent for a product mockup.

The three tools

Tool	What it does	Cost
plan_product_mockup	Dimensions, material, polygon budget, and a firm price — in about a second, because it produces a plan, not geometry	entry tier
order_product_mockup	Places the order and returns a job id immediately	asset tier
check_product_mockup	Polls the job and collects the asset when ready	free

No tool waits for generation. Ordering returns a job id, not a model — you poll for the result. That is not a design preference: the marketplace client severs any call at 30 seconds, and neither generation nor rendering reliably finishes inside that.

Surfaces: mug, hoodie, tshirt, phone case, tote, sticker, figurine, packaging.

Reading the outcome

A failed job names its cause and carries no asset. There is no placeholder and no degraded version — if the asset field is absent, nothing was produced.

An identical order inside the retention window returns the same job and does not charge again, so retrying after a dropped connection is safe.

Development

make up      # Postgres, Redis, and a fake generation backend under OrbStack
make check   # lint and types
make test    # integration suite against real infrastructure
make load    # k6, reporting avg / median / p95 / p99 and throughput

See CLAUDE.md for the invariants and the measured facts behind them, and docs/plans/ for the implementation plan.

Licence

MIT.