Flux MCP connector for AI agents

CALL ANY TOOL

Image generation end to end: submit renders, batch variations, dress subjects in reference garments, and track per-user credits.

fluxmcp_generate_image

Generate images

Submit one or more FLUX image generations from a text prompt, with optional reference images, and render up to 8 images in a parallel batch.

Parameters

Name

Type

Required

Description

requests

array

Required

List of image generation requests. Pass a single-item list for one image; pass multiple to generate a batch in one call (each entry is submitted to BFL in parallel and the iframe renders the results as a clickable grid). Cap is 8 entries per call.

fluxmcp_generate_variations

Generate variations

fluxmcp_vto

Virtual try-on

fluxmcp_get_history

Browse generation history

fluxmcp_get_credits

Check credits

fluxmcp_request_upload_url

Upload reference image

Build your Agent

Same auth pattern across LangChain, OpenAI, Anthropic, and Google ADK.

import { ScalekitClient } from "@scalekit-sdk/node";
import { createReactAgent } from "@langchain/langgraph/prebuilt";

const sk = new ScalekitClient(env.SCALEKIT_ENV_URL, env.SCALEKIT_CLIENT_ID, env.SCALEKIT_CLIENT_SECRET);

// Flux tools scoped to this user
const { tools } = await sk.tools.listScopedTools("user_123", {
  filter: { connectionNames: ["fluxmcp"], toolNames: [
    "fluxmcp_generate_image",
    "fluxmcp_generate_variations",
    "fluxmcp_get_history"] },
  pageSize: 100,
});

const agent = createReactAgent({ llm, tools });
await agent.invoke({ messages: [{ role: "user", content: "Generate a hero image of a mountain cabin at dusk." }] });

import OpenAI from "openai";
import { ScalekitClient } from "@scalekit-sdk/node";

const sk = new ScalekitClient(env.SCALEKIT_ENV_URL, env.SCALEKIT_CLIENT_ID, env.SCALEKIT_CLIENT_SECRET);
const openai = new OpenAI();

const { tools } = await sk.tools.listScopedTools("user_123", {
  filter: { connectionNames: ["fluxmcp"] }, pageSize: 100,
});

const res = await openai.chat.completions.create({
  model: "gpt-5",
  messages: [{ role: "user", content: "Create 4 variations of my last generation." }],
  tools,
});

// Execute the tool call with the user's vaulted Flux credential
await sk.tools.executeTool(res.choices[0].message.tool_calls[0], "user_123");

import Anthropic from "@anthropic-ai/sdk";
import { ScalekitClient } from "@scalekit-sdk/node";

const sk = new ScalekitClient(env.SCALEKIT_ENV_URL, env.SCALEKIT_CLIENT_ID, env.SCALEKIT_CLIENT_SECRET);
const anthropic = new Anthropic();

const { tools } = await sk.tools.listScopedTools("user_123", {
  filter: { connectionNames: ["fluxmcp"] }, pageSize: 100,
});

const msg = await anthropic.messages.create({
  model: "claude-sonnet-5",
  max_tokens: 1024,
  messages: [{ role: "user", content: "Show my recent Flux generations." }],
  tools,
});

// Tool call runs with the user's vaulted Flux credential
await sk.tools.executeTool(msg.content, "user_123");

import { Agent } from "@google/adk/agents";
import { ScalekitClient } from "@scalekit-sdk/node";

const sk = new ScalekitClient(env.SCALEKIT_ENV_URL, env.SCALEKIT_CLIENT_ID, env.SCALEKIT_CLIENT_SECRET);

const { tools } = await sk.tools.listScopedTools("user_123", {
  filter: { connectionNames: ["fluxmcp"] }, pageSize: 100,
});

const agent = new Agent({
  name: "flux_image_agent",
  model: "gemini-2.5-pro",
  instruction: "Generate and manage Flux images for the signed-in user.",
  tools,
});

await agent.run("Generate a product shot on a plain white background.");

Try these prompts

Copy any prompt into your agent. Each maps directly to a Flux tool. Click to copy, paste into your agent, done.

Generate images

Copy the prompt

Copied

Generate a hero image of a mountain cabin at dusk, 16:9.

Copy the prompt

Copied

Create 4 product shots of the ceramic mug on linen, rendered in parallel.

Copy the prompt

Copied

Dress the model in image 1 in the denim jacket from image 2.

Iterate and vary

Copy the prompt

Copied

Give me 4 variations of my last generation, same direction.

Copy the prompt

Copied

Use my uploaded sketch as a reference for the next render.

Copy the prompt

Copied

Rerun the rooftop scene as a batch of 3 alternatives.

Library and credits

Copy the prompt

Copied

Show my recent generations as a grid.

Copy the prompt

Copied

How many free generations do I have left?

Copy the prompt

Copied

Pull up the prompt and seed for yesterday's renders.

SEE HOW AUTH WORKS

Your users connect once. Their Flux credentials stay vaulted, every call is scope-checked, and every action is logged.

1

Authorize

Your user connects

Flux MCP

once. We tie it to their identity and the meetings they approved — no shared bot account, no org-wide access

Who:

user ‘A’

when:

Once per user

access:

Limited to user

2

Store

Their

Flux MCP

token lives in a vault scoped to them. User A's meetings are never reachable by an agent acting for user B, even on the same connection

vault:

encrypted

scope:

per-user

tokens:

auto-refreshed

3

Resolve

When your agent calls a

Flux MCP

tool, we fetch the right token server-side. It never touches your agent, never appears in the LLM context, never shows up in your logs

speed:

~40ms

check:

before every call

seen by:

nobody

4

Audit

Every

Flux MCP

tool call is logged — who triggered it, which meeting was fetched, what came back. 90 days of history, tied to the user who authorized it

history:

90 days

export:

SIEM-ready

logged:

every call

Test other agents

See the same per-user auth pattern across other AI connectors.

No items found.

Why Scalekit

Secure your agent's access. Connectors ship in minutes

Other connector libraries treat auth as a demo afterthought. Scalekit starts with identity, scope enforcement, and audit. Connectors follow.

01.

Shared tokens break per-user analytics

A shared Flux token looks fine in a demo. In production every render bills one account, credits drain with no attribution, and you cannot tell who generated what. Scalekit resolves the credential of the actual user who triggered the agent, never a shared bot.

// shared token
audit → bot_service_account

// scalekit
audit → user_abc ✓

02.

Authentication is not authorization

03.

Multi-tenancy is architectural

04.

Flux today. Others tomorrow.

“Our agents act across Salesforce, Gong, Google Drive, and more, on behalf of every customer. Scalekit behind the scenes meant we can keep adding tools without ever rebuilding how credentials or tool calling work.”

Venu Madhav Kattagoni

Head of Engineering / Von

FAQs

Frequently Asked Questions

Does the agent access Flux as the user or through a shared key?

As the user. Scalekit resolves the credential of the person who triggered the agent at request time, so every generation, variation, and upload in your audit trail is attributed to a real user, not a shared service account.

Where is the Flux token stored?

In an AES-256 encrypted vault with per-tenant namespacing. Tokens are resolved at request time, never enter LLM context, refresh automatically, and can be revoked from one dashboard.

Can I limit what the agent does in Flux?

Yes. Filter by tool name in listScopedTools to expose only what you want, for example generation and history without uploads or virtual try-on. Scalekit also enforces scope checks before every API call.

What happens when a user revokes access?

The credential is invalidated at the next tool call. The call fails closed, other users' connections are unaffected, and the revocation is logged in the audit chain.

Who pays when an agent generates images, and how are credits tracked?

The connected user. Every generation runs on that user's own BFL balance: free welcome generations are consumed first, then paid credits. Because Scalekit resolves per-user credentials, one user's batch never drains a teammate's pool, and get_credits reports each balance separately.

Flux MCP

Tools your creative agent reaches for on Flux, scoped per user.