Can my agent generate SVG vector files using Adobe Firefly?

Yes. Use the 'text_to_vector' tool. Provide a descriptive prompt, and the agent will command the Firefly engine to produce editable SVG vector graphics natively, which are perfect for use in Adobe Illustrator.

How do I perform generative fill on an image via chat?

First, use 'upload_image' to get an 'image_id'. Then, use the 'generative_fill' tool by providing the image ID, a mask ID (for the area to fill), and your prompt. The agent will execute the AI fill synchronously.

Can I remove the background of an image through the agent?

Absolutely. Use the 'remove_background' tool. After uploading your source image and retrieving its ID, the agent will command Firefly to process the file and return a transparent PNG body synchronously.

Adobe Firefly MCP Server for LlamaIndex 10 tools — connect in under 2 minutes

Name: Adobe Firefly
Availability: InStock
Author: Vinkius

docs.llamaindex.ai/en/stable/module_guides/tools/mcp

Built by Vinkius GDPR 10 Tools Framework

LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Adobe Firefly as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
    mcp_tool_spec = McpToolSpec(client=mcp_client)
    tools = await mcp_tool_spec.to_tool_list_async()

    agent = FunctionAgent(
        tools=tools,
        llm=OpenAI(model="gpt-4o"),
        system_prompt=(
            "You are an assistant with access to Adobe Firefly. "
            "You have 10 tools available."
        ),
    )

    response = await agent.run(
        "What tools are available in Adobe Firefly?"
    )
    print(response)

asyncio.run(main())

Fully ManagedVinkius Servers

60%Token savings

High SecurityEnterprise-grade

IAMAccess control

EU AI ActCompliant

DLPData protection

V8 IsolateSandboxed

Ed25519Audit chain

<40msKill switch

Stream every event to Splunk, Datadog, or your own webhook in real-time

* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About Adobe Firefly MCP Server

Connect your Adobe Firefly developer account to any AI agent and take full control of your commercially safe generative AI image and vector creation through natural conversation.

LlamaIndex agents combine Adobe Firefly tool responses with indexed documents for comprehensive, grounded answers. Connect 10 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.

What you can do

Text-to-Image Orchestration — Generate photorealistic or stylized images from text prompts utilizing Firefly's elite model 5 for high-fidelity output natively
Generative Fill & Expand — Fill masked areas or expand images beyond their borders by commanding absolute explicit text payloads to generate surrounding context flawlessly
Text-to-Vector Synthesis — Produce editable SVG vector graphics from descriptive prompts, bringing Adobe Illustrator-grade assets to your AI agent loops
Intelligent Image Editing — Upload source images to perform background removals, generate similar variations, or create object composites synchronously
Text Effects & Art — Transform plain textual strings into stylized visual art by applying AI-generated textures and effects according to style prompts
Asset Storage & Management — Manage uploaded image binaries and retrieve unique IDs to orchestrate complex multi-step generative operations securely
Model Discovery — Enumerate available Firefly models and versions to evaluate capabilities and determine precise active inference boundaries natively

The Adobe Firefly MCP Server exposes 10 tools through the Vinkius. Connect it to LlamaIndex in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect Adobe Firefly to LlamaIndex via MCP

Follow these steps to integrate the Adobe Firefly MCP Server with LlamaIndex.

Install dependencies

Run pip install llama-index-tools-mcp llama-index-llms-openai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save to agent.py and run: python agent.py

Explore tools

The agent discovers 10 tools from Adobe Firefly

Why Use LlamaIndex with the Adobe Firefly MCP Server

LlamaIndex provides unique advantages when paired with Adobe Firefly through the Model Context Protocol.

Data-first architecture: LlamaIndex agents combine Adobe Firefly tool responses with indexed documents for comprehensive, grounded answers

Query pipeline framework lets you chain Adobe Firefly tool calls with transformations, filters, and re-rankers in a typed pipeline

Multi-source reasoning: agents can query Adobe Firefly, a vector store, and a SQL database in a single turn and synthesize results

Observability integrations show exactly what Adobe Firefly tools were called, what data was returned, and how it influenced the final answer

Adobe Firefly + LlamaIndex Use Cases

Practical scenarios where LlamaIndex combined with the Adobe Firefly MCP Server delivers measurable value.

Hybrid search: combine Adobe Firefly real-time data with embedded document indexes for answers that are both current and comprehensive

Data enrichment: query Adobe Firefly to augment indexed data with live information before generating user-facing responses

Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying Adobe Firefly for fresh data

Analytical workflows: chain Adobe Firefly queries with LlamaIndex's data connectors to build multi-source analytical reports

Adobe Firefly MCP Tools for LlamaIndex (10)

These 10 tools become available when you connect Adobe Firefly to LlamaIndex via MCP:

generate_object

Instructions: Pass descriptive prompt. Generate an object composite image using Adobe Firefly

generate_similar

Instructions: Upload reference first, pass image_id and prompt. Generate images similar to a reference using Adobe Firefly

generative_expand

Instructions: Pass image_id, target width/height. Expand an image beyond its borders using Adobe Firefly

generative_fill

Instructions: Upload image first, get image_id and mask_id. Fill masked areas of an image using Adobe Firefly Generative Fill

list_models

List available Firefly models

remove_background

Instructions: Upload image first, pass image_id. Remove the background from an image using Adobe Firefly

text_effects

Instructions: Pass the text and a style prompt. Apply AI text effects using Adobe Firefly

text_to_image

Model 5 offers photorealistic output. Instructions: Pass prompt and count (1-4). Generate images from a text prompt using Adobe Firefly

text_to_vector

Instructions: Pass a descriptive prompt. Generate SVG vectors from a text prompt using Adobe Firefly

upload_image

Returns image ID. Instructions: Pass a publicly accessible URL. Upload an image to Adobe Firefly storage

Example Prompts for Adobe Firefly in LlamaIndex

Ready-to-use prompts you can give your LlamaIndex agent to start working with Adobe Firefly immediately.

"Generate a photorealistic image of a futuristic workspace with large windows"

"Create an SVG vector of a minimal mountain landscape"

"Remove the background from image 'img_789'"

Troubleshooting Adobe Firefly MCP Server with LlamaIndex

Common issues when connecting Adobe Firefly to LlamaIndex through the Vinkius, and how to resolve them.

BasicMCPClient not found

Install: pip install llama-index-tools-mcp

Adobe Firefly + LlamaIndex FAQ

Common questions about integrating Adobe Firefly MCP Server with LlamaIndex.

How does LlamaIndex connect to MCP servers?

Use the MCP client adapter to create a connection. LlamaIndex discovers all tools and wraps them as query engine tools compatible with any LlamaIndex agent.

Can I combine MCP tools with vector stores?

Yes. LlamaIndex agents can query Adobe Firefly tools and vector store indexes in the same turn, combining real-time and embedded data for grounded responses.