How to Use the Extracta MCP in LangChain

Q: Can LangChain handle file uploads directly through Extracta?

Yes, the agent uses uploadfileurl to send the document to the extraction engine. You do not need to write custom file-handling code in your LangChain chain.

Q: What happens if an Extracta extraction fails mid-chain in LangChain?

Your LangChain run catches the error from getresults or createextraction and can trigger a retry block. You can inspect the exact payload failure inside your LangSmith dashboard.

Q: Can I update active schemas on the fly?

You call updateextraction to modify your target fields without rebuilding your entire LangChain agent configuration. This updates the extraction rules instantly.

Feed clean, structured JSON from Extracta directly into your LangChain runs using this dedicated MCP Server.

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Connect Extracta MCP to LangChain

Create your Vinkius account to connect Extracta 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 Extracta with LangChain

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Route documents using LangChain and Extracta

The `create_classification` tool lets your LangChain agent inspect incoming documents and categorize them before starting heavy processing. Your agent passes a custom JSON schema defining your categories, gets the file type, and decides the next step in the chain. This setup prevents your pipeline from choking on unexpected file formats. You get immediate classification results without writing custom parsing code for every new document type.

Chain Extracta MCP Server steps with LangSmith

The `create_extraction` tool configures the exact fields your LangChain run needs to pull from a file. You define target fields like invoice dates or totals, and the agent initiates the run to get a tracking ID. LangSmith tracks the latency and token usage of this operation in real time. If an extraction fails, you see exactly which tool arguments caused the failure inside your active trace.

Poll for processed data inside LangChain runs

The `upload_file_url` tool sends document links directly to the API, returning a unique identifier for tracking. Your agent then uses `get_results` in a loop to check when the structured JSON is ready. This asynchronous pattern keeps your LangChain memory clean because the agent doesn't block while waiting for heavy PDF parsing. You pull the final structured JSON only when the API marks the job finished.

Setup guide

Set up Extracta 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 Extracta 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({
    "extracta-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 Extracta 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 Extracta. 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 Extracta MCP in LangChain

You register the server with the LangChain MCP adapter and pass the tools directly to your agent. This lets your agent call `create_extraction` and `get_results` natively as part of its reasoning loop.

Yes, the agent uses `upload_file_url` to send the document to the extraction engine. You do not need to write custom file-handling code in your LangChain chain.

Your LangChain run catches the error from `get_results` or `create_extraction` and can trigger a retry block. You can inspect the exact payload failure inside your LangSmith dashboard.

You call `update_extraction` to modify your target fields without rebuilding your entire LangChain agent configuration. This updates the extraction rules instantly.

The MCP server transmits document URLs and raw file links directly to the API for temporary processing. Your source files and extraction schemas are handled in an isolated sandbox, ensuring data does not linger in the local agent environment.

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