How to Use the Vectorizer AI MCP in LlamaIndex

Q: How does LlamaIndex use Vectorizer AI for design data?

LlamaIndex treats the output of vectorizeimage as a piece of knowledge. You index the generated vector files (like SVG or DXF) so that your RAG application can retrieve and discuss them later, grounding answers in actual API results.

Q: Can LlamaIndex manage design asset deletion?

Yes. By indexing records of deletions using deleteimage, you build a searchable log of what assets were removed and why. This helps maintain an auditable, knowledge-augmented record of your design lifecycle.

Q: What data types are indexed when using Vectorizer AI with LlamaIndex?

The server touches image tokens. When you run vectorizeimage, it converts bitmap images (JPG, PNG) into vector graphics, and the resulting file metadata is what gets integrated into your searchable index.

Q: Do I need to check account status for LlamaIndex when using Vectorizer AI?

It's a good practice. You can use getaccountstatus and store those results in your index. This lets you query the current billing situation as part of your application's knowledge base.

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Connect Vectorizer AI MCP to LlamaIndex

Create your Vinkius account to connect Vectorizer 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 Vectorizer AI with LlamaIndex

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Index Vectorized Designs for Semantic Search.

The `vectorize_image` tool converts bitmap images into scalable vector formats (SVG, EPS, PDF, DXF). When paired with LlamaIndex, the resulting metadata or generated vectors can be indexed. Instead of just running a job, you index the result. This allows your knowledge base to answer questions like, 'What was the SVG version of the logo we processed last month?'

Manage Design History in RAG Applications.

You can store and retrieve information about past design jobs using `download_image` alongside metadata. This makes the entire process searchable, not just the document content. LlamaIndex uses this data to build a unified index, so you query past sessions—for example, retrieving the file that was successfully generated by a previous run.

Track and Verify Design Tool Usage.

Use `get_account_status` to track usage metrics. By indexing these status reports, LlamaIndex builds an accurate history of your subscription health. This is useful for compliance or reporting features in a large RAG application; you can query the index and get answers grounded in actual billing data.

Setup guide

Set up Vectorizer 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 Vectorizer 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 Vectorizer AI tools.",
)
response = await agent.run("List recent Vectorizer 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 Vectorizer 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 Vectorizer AI MCP in LlamaIndex

LlamaIndex treats the output of `vectorize_image` as a piece of knowledge. You index the generated vector files (like SVG or DXF) so that your RAG application can retrieve and discuss them later, grounding answers in actual API results.

Yes. By indexing records of deletions using `delete_image`, you build a searchable log of what assets were removed and why. This helps maintain an auditable, knowledge-augmented record of your design lifecycle.

The server touches image tokens. When you run `vectorize_image`, it converts bitmap images (JPG, PNG) into vector graphics, and the resulting file metadata is what gets integrated into your searchable index.

It's a good practice. You can use `get_account_status` and store those results in your index. This lets you query the current billing situation as part of your application's knowledge base.

It allows you to store and retrieve specific file versions. You can index the result of `download_image` so that when a user asks for 'the final logo,' your system retrieves the exact file ID from the knowledge base.

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