G
GridShot
🇺🇸
Open Beta

From product catalog to approved high-resolution fashion shots — through one agent-ready workflow.

GridShot MCP gives AI agents a governed, end-to-end fashion production workflow: find the right model and garments, generate try-on grids, review the candidates, refine, and deliver publish-ready images — while you keep budget, permissions, and approvals in your hands. An operator layer for fashion production, not another image endpoint.

MCP endpoint
https://mcp.grid-shot.com/mcp
View tool reference

Calling an image model is the easy part

The real work is everything around the pixels: finding the right product, tracking outfit state, polling long jobs, reviewing candidates, retrying, editing, and delivering the final file. GridShot MCP hands agents that whole workflow, not just a render call.

Product and model lookup

An agent has to find the exact garment and the right model before it can generate anything. GridShot exposes searchable catalog and model discovery, so the agent works from your real inventory instead of guessing.

Job lifecycle and state

Try-on generation is asynchronous. Grids queue, run, and sometimes wait for approval. The agent polls a single operation for status and knows exactly when a result is ready — no brittle timing assumptions.

Review, retries, and delivery

Production means reviewing candidates, refining what is close, editing what needs a fix, and delivering one high-resolution file. Each of those is a full step of its own, with its own cost and its own controls.

One workflow, end to end

A single agent run moves from catalog lookup to a delivered, high-resolution image. Every step is a discrete, observable tool call.

  1. 01
    gridshot.search_modelsgridshot.search_garments

    Find the model and garments in your catalog

  2. 02
    gridshot.estimate_tryonFree

    Preview the cost before spending anything

  3. 03
    gridshot.create_tryonIdempotency-Keymax_cost_cents

    Generate a try-on grid with a spend ceiling

  4. 04
    gridshot.get_operation
    queuedrunningcompleted

    Poll one operation until the grid is ready

  5. 05
    gridshot.get_grid_previewgridshot.get_candidate_preview

    Review the grid and its individual candidates

  6. 06
    gridshot.refine_grid

    Refine the grid when a look is close but not final

  7. 07
    gridshot.deliver_candidate

    Deliver the chosen shot in high resolution

Your model and real product photos
Your model and real product photos
Your model and real product photos
Your model and real product photos
Generated try-on grid
Generated try-on grid
Refined grid
Refined grid
Delivered high-resolution shot
Delivered high-resolution shot

These are real outputs from this workflow, produced with the same tools an agent calls.

What agents can do

Sixteen tools cover the full production arc — discovery, composition, generation, review, refinement, and delivery.

Discover

Search your model library and garment catalog to pick the exact pieces a shot needs.

gridshot.search_modelsgridshot.search_garments

Compose and estimate

Assemble a model with one or more garments and preview the cost before any spend.

gridshot.estimate_tryon

Generate

Create a try-on grid of candidates and track the asynchronous job through to completion.

gridshot.create_tryongridshot.get_operation

Review

Pull the finished grid and inspect individual candidates through short-lived preview links.

gridshot.get_grid_previewgridshot.get_candidate_preview

Refine and edit

Refine a grid into a sharper one, or apply a targeted, coordinate-based edit to a single artifact.

gridshot.refine_gridgridshot.edit_artifact

Deliver

Turn the approved candidate into one publish-ready, high-resolution image.

gridshot.deliver_candidate

Why not just a try-on API?

A raw image endpoint answers one question: render this. Production needs the questions around it answered too.

A bare image endpoint

Takes an input, returns pixels. Your agent still has to find products, hold outfit state, invent polling, build review and retry logic, and figure out delivery — all on its own.

An operator layer

Discovery, cost estimation, generation, polling, review, refinement, and delivery are all modeled as tools over shared workspace state. The agent orchestrates a workflow instead of stitching one together.

Human-governed automation

Agents move fast; you stay in control. Spend, permissions, approvals, and history are enforced by GridShot, not by the agent.

A ceiling on every spend

max_cost_cents

Every paid action carries a maximum cost you set. If the real cost would exceed that ceiling, the call is refused rather than charged.

Least-privilege permissions

Each connection is limited to the scopes it was granted. An agent given read access to your catalog can never spend or deliver on your behalf.

Approval when it matters

approval_required

Some jobs pause and wait for a human decision before they continue, so a run can require sign-off instead of proceeding unattended.

A full trail

Every product, model, job, and asset an agent touches is the same record your team sees in the app — visible, attributable, and reviewable after the fact.

One workspace, three ways in

The products, models, jobs, and assets an agent works with over MCP are the exact same ones you manage in the GridShot app and over REST. No separate agent silo, no divergent state — one workspace, whether a person or an agent is driving.

Tool reference

Sixteen tools, grouped by the permission scope each one requires. Estimates and previews are free; just four tools ever spend.

Capabilities·No scope
gridshot.get_capabilitiesFree

Report the tools, scopes, and limits the current connection can use.

Catalog·catalog:read
gridshot.search_modelsFree

Search your model library for the right cast for a shot.

gridshot.search_garmentsFree

Search your garment catalog for the exact products to feature.

Try-on read·tryon:read
gridshot.get_operationFree

Check the status of an asynchronous job and learn when to poll again.

gridshot.get_gridFree

Retrieve a finished grid and its candidates.

gridshot.get_grid_previewFree

Get a short-lived preview link for a whole grid.

gridshot.get_candidate_previewFree

Get a short-lived preview link for a single candidate.

Try-on generation·tryon:generatetryon:read
gridshot.estimate_tryonFree

Preview the cost of a try-on grid before generating it.

gridshot.create_tryonPaid

Generate a grid of try-on candidates from a model and garments.

gridshot.estimate_refinementFree

Preview the cost of refining an existing grid.

gridshot.refine_gridPaid

Generate a refined grid from an existing one.

Delivery·candidate:delivertryon:read
gridshot.estimate_deliveryFree

Preview the cost of delivering a candidate in high resolution.

gridshot.deliver_candidatePaid

Produce one publish-ready, high-resolution image from a chosen candidate.

Artifact edits·artifact:edittryon:read
gridshot.preview_annotationsFree

Preview coordinate-based annotations on an artifact before editing it.

gridshot.estimate_editFree

Preview the cost of a targeted edit to an artifact.

gridshot.edit_artifactPaid

Apply one targeted, coordinate-based edit to an artifact.

Read tools need just their read scope. The three write capabilities — generate, deliver, and edit — additionally require the matching read scope, so an agent can always see what it just changed. Reading capabilities needs nothing more than a valid connection.

tryon:readtryon:generatecandidate:deliverartifact:editcatalog:read

Built for developers

A standards-based connection with OAuth, discovery, and predictable errors.

MCP endpoint
https://mcp.grid-shot.com/mcp
Streamable HTTP·2025-06-18
OAuth built in

User-driven agents connect with the authorization code flow and mandatory proof-key protection; server-side workloads use the client-credentials flow. Access tokens are short-lived, and refresh tokens rotate on every use.

https://grid-shot.com/.well-known/oauth-authorization-server

Supported clients

The Claude Code CLI is tested end to end — OAuth sign-in, consent, and tool calls all work. The claude.ai and Claude Desktop custom connector is provisioned as a registered client but is not yet verified. Other registered clients may connect but are not yet verified, and open client registration is disabled.

Asynchronous by design

Generation runs as a job. The agent polls a single operation, which reports its state and tells the agent how long to wait before checking again.

queuedrunningapproval_requiredcompletedfailedcancelled

Predictable errors

Missing permissions, insufficient balance, and exceeded cost ceilings come back as structured tool errors that say exactly what went wrong. A retry flag tells the agent when it is safe to try again.

INSUFFICIENT_SCOPEINSUFFICIENT_WALLET_BALANCECOST_LIMIT_EXCEEDEDretryable

Security and governance

Short-lived credentials, signed links, and scope checks on every call.

Short-lived access tokens

Access tokens expire within minutes, so a leaked token is useless almost immediately. Refresh tokens rotate, and a replayed one revokes its whole family.

Signed, expiring links

Preview and download links are signed and short-lived, and full-resolution images are never returned inline. Agents re-request a fresh link instead of caching one.

One-time service secrets

Service-account secrets are shown exactly once and stored as a one-way digest. Rotating a secret revokes the old one immediately, with no overlap window.

Scope checks per tool

Every tool verifies the connection holds the right permission before it runs. A connection can never reach beyond the scopes it was granted.

Costs and limits

Estimates are always free. Four actions can spend money, and each one names its own price.

Free

Free to explore

Every estimate and every annotation preview is free and never reserves funds or calls the image provider. An agent can plan and price a whole run before committing to anything.

Paid

Four paid actions

Generating a grid, refining a grid, delivering a high-resolution shot, and editing an artifact are the actions that spend. One generation produces a single grid of nine candidates; delivery renders one high-resolution file. Costs are quoted in US cents.

gridshot.create_tryongridshot.refine_gridgridshot.deliver_candidategridshot.edit_artifact

Every paid action requires a cost ceiling and a uniqueness key, so retries never double-charge and spend never exceeds what you allowed.Idempotency-Keymax_cost_cents

Get started in three steps

Self-serve today — create an account, connect your agent, and run your first try-on. No approval step and no access request needed.

01

Create a free account

Sign up for GridShot in about a minute. Every new workspace starts with $10 in credit, so you can explore and run real try-ons before spending your own money.

02

Connect your agent

Open Settings → MCP in the app and follow the built-in getting-started guide to connect Claude Code or create a service account for unattended, server-side workloads.

03

Run your first try-on

Point your agent at your catalog and generate your first 3×3 try-on grid — from catalog discovery all the way to a delivered, high-resolution shot.

Frequently asked questions

What is the GridShot MCP server?

It is a connection that lets AI agents run GridShot as a fashion production workflow — discovering products and models, generating try-on grids, reviewing, refining, and delivering high-resolution images — through a standard agent protocol, with your workspace controls enforced throughout.

Which agents and clients can connect?

The Claude Code CLI is verified end to end. The claude.ai and Claude Desktop custom connector is provisioned as a registered client but is not yet verified. Other registered clients may work but are not yet verified. Open client registration is turned off, so every connection is one you or a workspace admin approved.

How does billing work over MCP?

Estimates and annotation previews are always free and never reserve funds. Four actions can spend — generating a grid, refining a grid, delivering a shot, and editing an artifact — and each one requires a cost ceiling, so an agent can never spend more than you allow.

Do agents run fully on their own?

No. This is human-governed automation. Agents do the busywork, but spend limits, permission scopes, and approval steps are enforced by GridShot, and some jobs pause for a human decision before they continue.

How do I get started?

Create a free GridShot account — every new workspace starts with $10 in credit — then open Settings → MCP in the app. From there, follow the built-in getting-started guide to connect an MCP client or create a service account for unattended, server-side workloads. There is no approval step and no access request to file.

Where can I see everything an agent can do?

The tool reference on this page lists all sixteen tools, the permission scope each one requires, and which are free versus paid. It is the clearest way to understand the full surface before you connect.

Open Beta

Bring your agents to fashion production

GridShot MCP is in open beta — you can connect an agent today, and it is still maturing. Create a free account and put the full try-on-to-delivery workflow in reach of your agents.

Talk to us