How to Use the Pics.io MCP in LlamaIndex

Q: How do I search Pics.io assets with LlamaIndex?

You register the searchassets tool with your FunctionAgent. The agent then retrieves relevant assets based on your prompt.

Q: Does this Pics.io integration support historical data?

It does. You can use listrevisions to pull past metadata and build a timeline of changes in your LlamaIndex knowledge base.

Turn your Pics.io library into a queryable knowledge base using LlamaIndex MCP Server integration for instant asset retrieval.

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Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers — Included with Plan

Connect Pics.io MCP to LlamaIndex

Create your Vinkius account to connect Pics.io to LlamaIndex — we handle the hosting, security, and runtime updates so you don't have to. No server setup required.

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Setup Pics.io with LlamaIndex

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Semantic asset indexing with LlamaIndex

Index your asset metadata using `list_assets` and `get_asset` to make your entire library searchable via vector embeddings. You ask questions about your files, and LlamaIndex finds the right ones. This turns static storage into a live database. You get answers grounded in your actual media library, not guesses.

Revision tracking for RAG

Use `list_revisions` to feed historical file versions into your index. LlamaIndex can then reason about how an asset changed over time. It helps you build agents that understand the lifecycle of a file. You can query for the most recent version or compare metadata from previous iterations.

Unified knowledge retrieval

Combine `get_collection` and `search_assets` with other data sources in your index. You get a single interface for all your organizational data. Your RAG application uses these tools to pull live status updates from Pics.io. It ensures the agent always references current file states.

Setup guide

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

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