How to Use the PDF.co MCP in LangChain

Q: What is the setup process for using the PDF.co MCP Server with LangChain?

Install langchain-mcp-adapters and initialize the MultiServerMCPClient with your Vinkius endpoint. Then, pull the tools using client.gettools() and pass them directly to your agent constructor.

Q: How does the agent know if a PDF.co conversion job is finished?

The agent uses a polling loop with checkjobstatus. It submits an asynchronous job like pdftojson, checks the status identifier, and proceeds with the chain once the file is ready.

Q: How does PDF.co handle the privacy of my uploaded PDFs in LangChain?

Files processed by tools like pdftotext are temporarily stored in secure, encrypted storage and deleted after processing. Your Vinkius endpoint token keeps all API communication protected during agent execution.

Feed PDF.co tools directly into your LangChain multi-step reasoning chains and track every document conversion with LangSmith.

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Setup PDF.co with LangChain

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Multi-step data extraction pipelines

The `pdf_to_json` tool acts as the starting point for your LangChain document processing chains. Your agent runs this tool to extract raw layout data, then feeds that exact JSON structure directly into subsequent LLM analysis steps. You track the entire extraction process in LangSmith, which logs the latency and token usage for each document conversion. If the document is too large, the agent dynamically switches to `split_pdf` to process smaller chunks in parallel before merging results.

Dynamic OCR and table parsing in LangChain

The `ocr_image` tool translates scans into machine-readable text inside your ReAct agent loops. When your chain encounters a scanned invoice, the agent detects the image format and calls this tool before passing the text to a database integration. For structured financial documents, the agent triggers `pdf_to_csv` instead. This keeps your LangChain pipelines clean by converting tables into clean CSV strings that feed straight into your agent's analytical prompt context.

Secure document handling via LangChain MCP Server

The `protect_pdf` tool secures your generated reports before they leave your LangChain pipeline. Your agent automatically applies passwords and encryption to output files right after combining raw files with the `merge_pdfs` tool. This setup runs entirely through a single secure MCP Server endpoint, removing the need to manage different API clients for each document task. Your agent handles the workflow, and LangSmith records the input and output parameters for every security operation.

Setup guide

Set up PDF.co 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 PDF.co 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({
    "pdfco-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 PDF.co 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 PDF.co. 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 PDF.co MCP in LangChain

Look, here's the thing: you configure LangSmith to monitor the tool calls. Every time your agent calls `pdf_to_json` or `ocr_image`, LangSmith logs the input URL, processing latency, and the final extracted text payload automatically.

Yes, your agent manages file size limits by chaining tools. The agent calls `split_pdf` to break down a massive document, processes the pages, and then uses `merge_pdfs` to assemble the final output.

Install `langchain-mcp-adapters` and initialize the `MultiServerMCPClient` with your Vinkius endpoint. Then, pull the tools using `client.get_tools()` and pass them directly to your agent constructor.

The agent uses a polling loop with `check_job_status`. It submits an asynchronous job like `pdf_to_json`, checks the status identifier, and proceeds with the chain once the file is ready.

Files processed by tools like `pdf_to_text` are temporarily stored in secure, encrypted storage and deleted after processing. Your Vinkius endpoint token keeps all API communication protected during agent execution.

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