How to Use the Uploadcare MCP in LangChain

Q: Can I retrieve file details using Uploadcare and LangChain?

Yep. The getfiledetails tool lets you pull technical metadata for any specific asset ID. This is useful when your multi-step reasoning needs to know the format or size of a file before passing it to another service.

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Connect Uploadcare MCP to LangChain

Create your Vinkius account to connect Uploadcare to LangChain 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 Uploadcare with LangChain

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Perplexity

Execute complex file operations using LangChain

The `batch_delete_files` tool lets your agent permanently remove multiple files in a single operation. This is critical for cleanup routines, ensuring that entire sets of assets get cleared out when they're no longer needed. You can also use `list_files` to pull down file lists, supporting pagination via limit. Your LangChain agent processes this list, determines which files need action, and then executes the delete or store commands.

Manage project metadata with the MCP Server

Need to know what's in your project? The `get_project_info` tool gives you project-level metadata and usage statistics. This lets your ReAct agent check resource limits or confirm billing details before proceeding. Furthermore, if you need specifics on a file collection, the `list_file_groups` tool lists those immutable groups. Your pipeline uses this group data to contextually decide which files are grouped together.

Copy and store assets using LangChain

The `copy_file` tool copies an existing file either locally or to a remote storage location, giving you redundancy. Before the copy happens, your agent might use `get_file_details` to verify the original file's technical metadata. Once the file is safely copied, the `store_file` tool marks that temporary asset as permanently stored in Uploadcare, completing the secure lifecycle.

Setup guide

Set up Uploadcare 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 Uploadcare 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({
    "uploadcare-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 Uploadcare transactions"
    })
    print(result["messages"][-1].content)

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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 Uploadcare. 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 Uploadcare MCP in LangChain

LangChain agents manage scale by breaking tasks into smaller steps. Instead of one massive command, the agent first uses `list_files` to paginate through file lists. It then processes these chunks sequentially using tools like `store_file`, keeping the whole operation observable in LangSmith.

The agent handles failure by logging the specific files that caused the error. Since `batch_delete_files` is irreversible, your chain logic must check for success codes after the call and decide whether to retry or halt the pipeline.

Yep. The `get_file_details` tool lets you pull technical metadata for any specific asset ID. This is useful when your multi-step reasoning needs to know the format or size of a file before passing it to another service.

The server primarily touches project metadata and references to file groups. Specifically, tools like `get_group_details` read information about those collections of files within your uploaded assets.

The primary limitation is always the scope of the tool call. If you need to query historical project usage, you must explicitly use `get_project_info`. The agent won't guess that data; it needs a specific tool invocation.

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