Modelry

@modelry list all workspaces and show me which ones have active embeds deployed

Based on the Modelry API response, you have 3 workspaces: 'Production' (ID: ws_abc123) contains 7 active embeds, 'Staging' (ID: ws_def456) has 2 embeds, and 'Archive' (ID: ws_ghi789) has no embeds currently deployed. The Production workspace was last updated 3 days ago and includes embeds for your main product documentation and customer support flows.

This example demonstrates read-heavy workspace inspection. Requires API key with workspace:read scope. The MCP retrieves workspace metadata and embed counts, but won't show embed content or configuration details without additional calls to Get Embed for each ID.

@modelry create a new workspace called 'Q1-2025-Launch' for our upcoming product release embeds

Workspace 'Q1-2025-Launch' created successfully with ID ws_jkl012. The workspace is now active and ready to receive products and embeds. You can begin uploading product repositories or creating embeds within this workspace. Note that the workspace inherits your account's default settings for embed behavior and access controls.

This write example shows workspace creation, a prerequisite for organizing Modelry products and embeds. The MCP executes the create action immediately—there's no preview or undo. Workspace names must be unique within your account, and creation counts against your plan's workspace limit.

@modelry review all products in workspace ws_abc123 and draft a summary of which ones haven't been updated in over 90 days

Analyzing the 12 products in workspace ws_abc123: 4 products show no updates in the past 90 days. 'Legacy API Docs' (last updated 147 days ago) and 'v1 Integration Guide' (last updated 203 days ago) are the oldest. 'Mobile SDK Reference' and 'Deprecated Webhooks' also exceed the 90-day threshold. These stale products may contain outdated information if your APIs or features have evolved. Consider archiving or refreshing them before the Q1 launch.

This synthesis example pairs Modelry's product repository metadata with AI reasoning to identify maintenance gaps. The MCP fetches product details but doesn't auto-update or archive anything—you must confirm deletion or updates separately. Useful for audit workflows before major releases.

When Modelry wins for agencies managing client 3D catalogs

A 6-person creative agency builds product configurators for furniture brands. Each client gets a dedicated workspace with its own embeds and product repositories. Modelry's workspace-first architecture makes this clean: one API key, 15 tools to spin up, tear down, and query client environments without cross-contamination. The create/delete pattern across workspaces, products, and embeds maps directly to client onboarding and offboarding. If you're managing fewer than 3 clients or your products don't need 3D/AR embeds, the abstraction overhead isn't worth it—stick with a flat file structure. But once you hit 4+ concurrent client projects with visual product data, Modelry's isolation model pays off. Set this up if your agency contract includes product catalog handoff.

How Modelry handles pre-launch product repository staging

A 3-person DTC brand team launches 12 new SKUs quarterly, each with 3D viewer embeds for the product page. Two weeks before launch, they stage products in a test workspace, validate embed rendering across devices, then migrate repositories to the live workspace on launch day. Modelry's get/delete repository tools let the team script this migration: pull repository details from staging, recreate in production, delete the staging copy. The idempotent delete behavior (404 treated as success) means the script won't break if someone manually cleaned up. If your launch cadence is slower than monthly or you're not using embeds, the workspace overhead is overkill—just version your assets in S3. But for high-frequency visual product drops, Modelry's repository model keeps staging and production separate without manual file juggling. Worth it at 6+ launches per year.

When to use Modelry for seasonal campaign teardown

A 5-person marketing team runs quarterly campaigns with custom AR try-on embeds for limited-edition products. After each campaign, they need to delete 20-40 embeds and their backing product repositories to avoid API quota bloat. Modelry's delete tools handle this in a loop: the idempotent 404 logic means the script can run twice without errors, and the workspace scope keeps campaign assets isolated from evergreen products. The 15-tool surface is more than you need for pure cleanup, but the get-then-delete pattern (retrieve embed details, confirm ID, delete) prevents accidental deletions. If your campaigns reuse the same embeds or you run fewer than 3 per year, manual deletion through the Modelry dashboard is faster. Automate this once you're deleting 50+ embeds annually and your team forgets which campaign owns which asset.