Can my agent create a new data extraction setup with custom fields?

Yes. Use the 'create_extraction' tool. Provide a JSON schema defining the fields you expect (e.g., 'total_amount', 'vendor_name'). The agent will return a new extractionId for document processing.

How do I process a PDF document using a specific extraction ID via chat?

Use the 'upload_file_url' tool. Provide the extractionId and the public URL of your PDF. The agent will trigger the workflow and return a documentId, which you can use with 'get_results' to fetch the data.

Can I see the predicted document type and confidence score through the agent?

Absolutely. Use the 'get_classification_results' tool with the document and classification IDs. The agent will retrieve the AI-predicted label (e.g., 'Invoice') and the confidence score for the processed file.

Extracta MCP Server for LangChain 10 tools — connect in under 2 minutes

Name: Extracta
Availability: InStock
Author: Vinkius

python.langchain.com/docs/integrations/tools/mcp

Built by Vinkius GDPR 10 Tools Framework

LangChain is the leading Python framework for composable LLM applications. Connect Extracta through Vinkius and LangChain agents can call every tool natively. combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

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

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    async with MultiServerMCPClient({
        "extracta": {
            "transport": "streamable_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,
        )
        response = await agent.ainvoke({
            "messages": [{
                "role": "user",
                "content": "Using Extracta, show me what tools are available.",
            }]
        })
        print(response["messages"][-1].content)

asyncio.run(main())

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* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About Extracta MCP Server

Connect your Extracta.ai account to any AI agent and take full control of your automated data extraction and document classification through natural conversation.

LangChain's ecosystem of 500+ components combines seamlessly with Extracta through native MCP adapters. Connect 10 tools via Vinkius and use ReAct agents, Plan-and-Execute strategies, or custom agent architectures. with LangSmith tracing giving full visibility into every tool call, latency, and token cost.

What you can do

Extraction Orchestration — Create and configure new data extraction processes by defining JSON schemas for fields like dates, amounts, and item descriptions natively
Live Document Processing — Submit publicly accessible file URLs (PDF, JPG, PNG) to trigger asynchronous extraction workflows and retrieve structured JSON data seamlessly
AI Classification — Set up document classification rules to automatically sort documents into types like invoices, receipts, or contracts based on AI predictions
Result Auditing — Retrieve extraction status and finalized structured data for specific documents, evaluating confidence scores and predicted categories flawlessly
Batch History Monitoring — Fetch paginated lists of previously extracted documents and their associated data payloads to track historical processing limitlessly
Configuration Mutation — Update existing extraction settings and mapping rules without creating new endpoints to refine your data parsing logic
Workflow Management — View and manage extraction and classification configurations, including configured fields and webhook settings securely

The Extracta MCP Server exposes 10 tools through the Vinkius. Connect it to LangChain in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect Extracta to LangChain via MCP

Follow these steps to integrate the Extracta MCP Server with LangChain.

Install dependencies

Run pip install langchain langchain-mcp-adapters langgraph langchain-openai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save the code and run python agent.py

Explore tools

The agent discovers 10 tools from Extracta via MCP

Why Use LangChain with the Extracta MCP Server

LangChain provides unique advantages when paired with Extracta through the Model Context Protocol.

The largest ecosystem of integrations, chains, and agents. combine Extracta MCP tools with 500+ LangChain components

Agent architecture supports ReAct, Plan-and-Execute, and custom strategies with full MCP tool access at every step

LangSmith tracing gives you complete visibility into tool calls, latencies, and token usage for production debugging

Memory and conversation persistence let agents maintain context across Extracta queries for multi-turn workflows

Extracta + LangChain Use Cases

Practical scenarios where LangChain combined with the Extracta MCP Server delivers measurable value.

RAG with live data: combine Extracta tool results with vector store retrievals for answers grounded in both real-time and historical data

Autonomous research agents: LangChain agents query Extracta, synthesize findings, and generate comprehensive research reports

Multi-tool orchestration: chain Extracta tools with web scrapers, databases, and calculators in a single agent run

Production monitoring: use LangSmith to trace every Extracta tool call, measure latency, and optimize your agent's performance

Extracta MCP Tools for LangChain (10)

These 10 tools become available when you connect Extracta to LangChain via MCP:

create_classification

g. invoice, receipt, contract). Pass JSON schema defining categories. Create a new Extracta document classification setup

create_extraction

g. language, format, expected fields like invoice_date, total_amount). Returns a new extractionId used for subsequent document processing. Create a new Extracta.ai data extraction process

delete_extraction

Subsequent uploads to this extractionId will fail. Delete an Extracta.ai extraction process

get_batch_results

Get bulk historical results from an Extraction process

get_classification_results

Get the predicted document category from Extracta

get_results

If not completed, it will indicate processing status. Get extraction results for a specific document

update_extraction

Modifies mapping rules without needing to create a new endpoint. Update an existing Extracta extraction configuration

upload_file_url

Returns a documentId. Use ea.get_results to poll for extracted data. Upload a document URL to Extracta for processing

view_classification

View details of an existing document classification process

view_extraction

View configuration of an existing Extracta extraction process

Example Prompts for Extracta in LangChain

Ready-to-use prompts you can give your LangChain agent to start working with Extracta immediately.

"Create an extraction process for invoices with fields: date, vendor, total"

"Extract data from this receipt URL: https://example.com/receipt.pdf"

"What type of document is doc_789 according to my classification rules?"

Troubleshooting Extracta MCP Server with LangChain

Common issues when connecting Extracta to LangChain through the Vinkius, and how to resolve them.

MultiServerMCPClient not found

Install: pip install langchain-mcp-adapters

Extracta + LangChain FAQ

Common questions about integrating Extracta MCP Server with LangChain.

How does LangChain connect to MCP servers?

Use langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.

Which LangChain agent types work with MCP?

All agent types including ReAct, OpenAI Functions, and custom agents work with MCP tools. The tools appear as standard LangChain tools after the adapter wraps them.