How to Use the Pics.io MCP in LangChain

Q: How do I perform a search in Pics.io using LangChain?

You pass searchassets to your agent as a tool. The LangChain agent then handles the query and returns the specific asset data you need.

Build multi-step media pipelines in LangChain by chaining Pics.io asset tools for automated metadata management and collection routing.

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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 LangChain

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

GDPR Included with Plan

Setup Pics.io with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity Vinkius runs on Gemini

Gemini

Grok

Automate asset chains in LangChain

Chain `list_assets` and `update_asset` to automatically sync metadata across your library. You define the logic, and the agent executes the sequence without human intervention. Each step is tracked in LangSmith so you can see exactly how the agent processed your files. It’s perfect for building complex workflows that need to handle thousands of assets.

Dynamic collection management

Use `create_collection` and `list_collections` to build responsive storage structures. The agent evaluates current project needs and builds the directory tree for you. This MCP Server provides the raw operations needed to build custom agents that respond to incoming file uploads. Your pipeline stays organized as the agent manages the hierarchy.

Context-aware asset feedback

Integrate `add_comment` and `list_comments` directly into your reasoning loop. Agents can read feedback from reviewers and update asset status based on that text. You avoid manual ticket tracking by letting the agent parse comments and trigger the next step in your chain. It keeps your creative feedback loop moving at machine speed.

Setup guide

Set up Pics.io MCP in LangChain

Prerequisites

Python 3.10+ installed
langchain-mcp-adapters + langgraph packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install langchain-mcp-adapters langgraph langchain-openai. The MCP adapters package converts MCP tools into native LangChain BaseTool objects.
2

Connect via HTTP transport
Use MultiServerMCPClient with "transport": "http" pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create a ReAct agent
Pass the discovered tools to create_react_agent() from LangGraph. The agent automatically routes Pics.io tool calls through the MCP protocol.
4

Run with any LLM
Swap ChatOpenAI for ChatAnthropic, ChatGoogleGenerativeAI, or any LangChain-compatible model. The MCP tools work identically across all providers.

agent.py

from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI

async with MultiServerMCPClient({
    "picsio-mcp": {
        "transport": "http",
        "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
    }
}) as client:
    tools = client.get_tools()

    agent = create_react_agent(
        ChatOpenAI(model="gpt-4o"),
        tools,
    )
    result = await agent.ainvoke({
        "messages": "List recent Pics.io transactions"
    })
    print(result["messages"][-1].content)

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