How to Use the autoRetouch MCP in LlamaIndex

Index your autoRetouch image data into LlamaIndex for semantic search and retrieval.

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Connect autoRetouch MCP to LlamaIndex

Create your Vinkius account to connect autoRetouch 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 autoRetouch with LlamaIndex

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Ground LlamaIndex queries in image data

Index the results from `list_images` and `get_image` into your vector store. This turns static API data into searchable context for your RAG applications. You can ask about specific product edits, and the system retrieves the actual state from your account.

Sync autoRetouch workflows with LlamaIndex

Use `list_workflows` to map out your available editing processes. Once indexed, the agent knows exactly which workflows to apply based on your document queries. It removes the guesswork by grounding agent actions in your live account configuration.

Query batch history via LlamaIndex

Retrieve historical data using `list_batches` to inform future processing decisions. The agent builds a knowledge base of past performance to optimize new tasks. This creates a feedback loop where your current processing strategy relies on proven results.

Setup guide

Set up autoRetouch 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 autoRetouch 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 autoRetouch tools.",
)
response = await agent.run("List recent autoRetouch data")

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by autoRetouch. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

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Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about autoRetouch MCP in LlamaIndex

Absolutely. By using the McpToolSpec, you can ingest execution logs into your index. This allows for semantic searching across your previous retouching jobs.

Use the llama-index-tools-mcp package. Instantiate the BasicMCPClient with your Vinkius URL and pass the resulting tools to your FunctionAgent.

It will. The tools provide structured output that LlamaIndex parses into your vector store, making every workflow detail queryable.

It depends on how you configure your agent. You can cache tool results in your index to minimize API calls while keeping your data grounded.

Access is restricted to your specific Vinkius endpoint token. Your image identifiers are indexed locally within your own vector store, keeping the raw data isolated.

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