How to Use the Claid AI MCP in LlamaIndex

Index your image processing history into LlamaIndex to build knowledge bases grounded in actual visual data.

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Connect Claid AI MCP to LlamaIndex

Create your Vinkius account to connect Claid AI 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 Claid AI with LlamaIndex

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Ground LlamaIndex queries in Claid AI data

Use `list_claid_collections` to pull your existing image sets into your index. LlamaIndex turns these results into a queryable knowledge base that your agent can reference. Instead of hallucinating about past edits, your agent retrieves actual data from your account. It's the best way to keep your RAG application grounded in your specific product photo history.

Automate background removal via LlamaIndex

The `remove_image_background` tool integrates directly into your agent's tool set. LlamaIndex indexes the result of these edits, making them searchable for future context. Your agent can now answer questions about which products have had their backgrounds stripped. It turns raw API actions into structured knowledge that remains available for your RAG pipeline.

Index async tasks for LlamaIndex retrieval

Track task results with `get_processing_task_details` and feed the output into your vector store. This creates a permanent record of every resolution upscale or edit you perform. Your LlamaIndex implementation gains deep visibility into the history of your visual assets. It allows for complex queries about processing status and results that aren't possible with standard logs.

Setup guide

Set up Claid AI 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 Claid AI 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 Claid AI tools.",
)
response = await agent.run("List recent Claid AI 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 Claid AI. 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.

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Common questions about Claid AI MCP in LlamaIndex

Yes, the tool outputs are structured data that LlamaIndex consumes easily. You can index the results of your image processing tasks to make them searchable via natural language.

By providing a bridge between raw image files and structured metadata. The agent uses the tools to edit photos and then stores the resulting metadata in your index.

You can absolutely use it for that purpose. Whenever an image is processed, the tool provides the details necessary to update your index with the latest file information.

Data privacy is handled at the transport layer using encrypted tokens. Only your authorized agent can request the image metadata, which is then stored in your private vector store.

You just need to define the tool spec for the server. Once defined, LlamaIndex handles the mapping of the image processing tools into your agent's available tool list.

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