How to Use the imgix (Real-time Image Processing) MCP in LlamaIndex

Q: How do I install the imgix (Real-time Image Processing) MCP Server for LlamaIndex?

Run pip install llama-index-tools-mcp to get the package. Initialize the BasicMCPClient with your Vinkius URL, wrap it in a McpToolSpec, and pass it to your FunctionAgent.

Q: Can LlamaIndex index asset metadata fetched from imgix (Real-time Image Processing)?

Yes. The agent calls listassets to pull file paths and sizes, then converts that metadata into documents that are loaded directly into your vector index.

Q: How do I verify CDN source status using this MCP Server in LlamaIndex?

Your agent calls getsource with the target source ID. The returned status, domain, and deployment type are used by the agent to make routing decisions.

Q: Does the imgix (Real-time Image Processing) server support bulk updates in LlamaIndex?

You can write an agent loop that runs updatesource across multiple configurations. The agent reads the desired state from your index and applies the changes sequentially.

Index and query your imgix CDN source data directly from your LlamaIndex knowledge base.

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Connect imgix (Real-time Image Processing) MCP to LlamaIndex

Create your Vinkius account to connect imgix (Real-time Image Processing) 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 imgix (Real-time Image Processing) with LlamaIndex

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Index CDN Source Metadata into LlamaIndex RAG

The `list_sources` tool pulls all active CDN endpoints configured in your account. This MCP Server makes your CDN infrastructure searchable by loading these configurations directly into your vector store. You can query your knowledge base to find which domains are live without hitting the raw API. This eliminates guesswork when developers need to find active image hosts. The agent queries the index, retrieves the correct source ID, and uses `get_source` to verify its origin settings.

Semantic Search Over Asset Inventories

The `list_assets` tool lists file paths, sizes, and content types from your CDN. LlamaIndex converts this raw asset data into vector embeddings for semantic search. Users can search for specific media assets using natural language instead of exact file paths. When a match is found, your agent uses `get_asset` to pull the precise metadata. This turns a flat file list into an intelligent, searchable media catalog.

Automated Cache Maintenance via Index Queries

The `purge` tool removes cached assets and their derivatives from the CDN. Your LlamaIndex agent analyzes user queries about broken or outdated images to locate the offending file in the index. Once identified, it executes the cache clear automatically. This loop keeps your CDN aligned with your actual asset storage. You do not need manual cron jobs to clean up stale files; the MCP agent handles invalidation based on real-time search results.

Setup guide

Set up imgix (Real-time Image Processing) 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 imgix (Real-time Image Processing) 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 imgix (Real-time Image Processing) tools.",
)
response = await agent.run("List recent imgix (Real-time Image Processing) 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 imgix. 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 imgix (Real-time Image Processing) MCP in LlamaIndex

Run `pip install llama-index-tools-mcp` to get the package. Initialize the `BasicMCPClient` with your Vinkius URL, wrap it in a `McpToolSpec`, and pass it to your `FunctionAgent`.

Yes. The agent calls `list_assets` to pull file paths and sizes, then converts that metadata into documents that are loaded directly into your vector index.

Your agent calls `get_source` with the target source ID. The returned status, domain, and deployment type are used by the agent to make routing decisions.

You can write an agent loop that runs `update_source` across multiple configurations. The agent reads the desired state from your index and applies the changes sequentially.

Your imgix API keys, source domains, and asset paths are handled with a zero-trust model. Vinkius executes all tool calls inside transient containers that destroy themselves post-run. We never write your metadata or configuration details to persistent storage.

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