How to Use the Midjourney MCP in LlamaIndex

Q: Can LlamaIndex query Midjourney task history?

By calling gettasks, your agent can retrieve recent generation records. These are then indexed, allowing you to ask questions about your past creative output.

Turn your generated assets into searchable knowledge by indexing Midjourney outputs directly into LlamaIndex structures.

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Connect Midjourney MCP to LlamaIndex

Create your Vinkius account to connect Midjourney to LlamaIndex and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Midjourney with LlamaIndex

Ask AI about this MCP

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Claude

Perplexity

Index generated prompts and images

Use `describe` to feed semantic data into your LlamaIndex vector store. Every image you create becomes a searchable record linked to its original prompt and style parameters. This turns your generation history into a library. You can query your past work to find the right style for new projects without hunting through chat logs.

Ground your responses in API data

Your RAG applications can pull live status updates using `get_tasks`. This ensures your agent provides answers based on the actual state of your generation queue. Forget guessing if a job is done. Your index reflects the current reality of the Midjourney API, making your knowledge base accurate and reliable.

Automate metadata extraction

Configure your agents to run `describe` on every successful `imagine` result. This metadata is automatically ingested by LlamaIndex, enriching your documents with visual context. You gain a searchable history of your creative process. It turns raw images into structured data that your agents can actually reason about.

Setup guide

Set up Midjourney MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Midjourney MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

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

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Midjourney tools.",
)
response = await agent.run("List recent Midjourney data")

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Common questions about Midjourney MCP in LlamaIndex

You use the MCP tool spec to pull image metadata and pass it to your document loader. This populates your vector store with meaningful visual descriptions.

By calling `get_tasks`, your agent can retrieve recent generation records. These are then indexed, allowing you to ask questions about your past creative output.

Your image history is handled via an ephemeral connection. Only the data you explicitly choose to index stays in your local vector store.

You set up a periodic refresh cycle. The agent checks for new tasks, updates the index, and keeps your knowledge base current.

It stores the URLs and associated metadata. Your system retrieves the images from the provided links only when you need to display them.

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OpenAI Agents SDK sdk-python