How to Use the Getty Images MCP in LlamaIndex

Q: How do I configure LlamaIndex to use Getty Images?

Install llama-index-tools-mcp. Set up a BasicMCPClient with your Vinkius URL, wrap it in McpToolSpec, and call totoollistasync() to pass the functions to your agent.

Q: Can LlamaIndex filter out editorial Getty Images?

Yes. You use the allowedtools filter when passing the spec to your agent. Just include searchcreative and omit searcheditorial from the list.

Q: Does LlamaIndex automatically download the Getty Images files?

No. The agent usually relies on searchimages for previews and metadata. It only executes downloadimage if explicitly instructed to pull the full asset.

Index Getty Images metadata into LlamaIndex vector stores to build searchable, media-rich knowledge bases.

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Connect Getty Images MCP to LlamaIndex

Create your Vinkius account to connect Getty Images 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 Getty Images with LlamaIndex

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Index Getty Images via MCP Server

LlamaIndex handles stock media differently. Instead of just running a search and returning a link, your agent calls `search_images` and ingests the resulting titles, captions, and preview URLs directly into a vector store. This turns transient API responses into a persistent, queryable index. When a user asks for photos matching a specific brand guideline, the agent queries the local index first. If it needs more context, it runs `get_similar` on a known asset ID and embeds the new metadata alongside the existing records.

Grounded answers with real metadata

Hallucinations ruin media planning. By connecting the Getty MCP tools, your `FunctionAgent` fetches concrete data using `get_image` or `get_video`. The agent reads the actual resolution, licensing tags, and release status straight from the API. You get answers backed by reality. If someone asks if a specific video is available for commercial use, the agent checks the indexed output from `search_creative` rather than guessing based on its training data.

Build semantic search over lightboxes

Your team probably has thousands of saved assets. The agent can run `list_collections` to pull your Getty lightboxes, then use `get_images_batch` to grab the details for every saved photo. LlamaIndex embeds all of this text so you can search it locally. Now you chat with your lightboxes. A user describes a vibe, and LlamaIndex performs a semantic search over the ingested captions to find the exact asset ID. Once found, it triggers `download_image` to pull the high-res file.

Setup guide

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

Install `llama-index-tools-mcp`. Set up a `BasicMCPClient` with your Vinkius URL, wrap it in `McpToolSpec`, and call `to_tool_list_async()` to pass the functions to your agent.

Yes. You use the `allowed_tools` filter when passing the spec to your agent. Just include `search_creative` and omit `search_editorial` from the list.

No. The agent usually relies on `search_images` for previews and metadata. It only executes `download_image` if explicitly instructed to pull the full asset.

Yes. You can index a PDF of brand guidelines alongside the JSON output from `search_videos`. The agent cross-references both sources to recommend compliant media.

The integration handles sensitive asset IDs, download URLs, and proprietary search phrases. The protocol uses a zero-trust architecture, requiring only a single endpoint token. Data flows directly to your local vector store without lingering on intermediary databases.

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