How to Use the Docamatic MCP in LangChain

Q: Can I trace Docamatic template rendering errors inside a LangChain pipeline?

Yes. Every call to generatepdffromtemplate is captured by LangChain's native tracing, showing you the exact JSON payload sent and any rendering errors returned by the server.

Q: How do I combine multiple generated invoices using Docamatic and LangChain?

Use the mergemultiplepdfs tool. Your agent collects the paths of individual PDFs generated in previous chain steps and passes them as a list to the merge tool to produce a single combined document.

Build multi-step document workflows where your LangChain agent generates, edits, and merges PDFs based on live chain data.

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Connect Docamatic MCP to LangChain

Create your Vinkius account to connect Docamatic 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 Docamatic with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Chain HTML rendering into multi-step PDF workflows

Your LangChain agent can grab data from a database, pass it directly to `generate_pdf_from_html`, and feed the resulting file path straight to the next node in your Graph. Because LangChain treats every tool call as a traceable step, you can monitor the exact HTML payload and latency in LangSmith without guessing why a render failed. If you need to combine multiple reports, the agent takes the output array from previous steps and feeds it directly into `merge_multiple_pdfs`. The entire sequence runs within a single run trace, keeping your document generation pipeline completely transparent and easy to debug.

Automate custom invoices using the Docamatic MCP Server

Stop writing custom PDF generation code for every client request. Your LangChain agent inspects incoming JSON payloads, matches them against the schemas returned by `list_available_builtin_templates`, and triggers `generate_pdf_from_template` to output invoices or packing slips instantly. This setup lets your chain decide which layout fits the data structure on the fly. The agent handles the decision logic, calls the tool, and passes the finished document path to your email or storage node without human intervention.

Trace and modify existing documents mid-chain

When your chain requires updating a document with fresh data, the agent invokes `add_elements_to_pdf` to overlay tracking numbers or signatures. You can see the exact coordinate inputs and text strings inside your LangSmith dashboard to verify placement accuracy. If a step fails, you don't have to guess. The agent checks the document history via `list_generation_history` to verify the state before trying the write operation again, keeping your automated workflows resilient.

Setup guide

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

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Common questions about Docamatic MCP in LangChain

The server processes rendering asynchronously. Your LangChain agent receives the file path once `generate_pdf_from_html` completes, allowing you to pass the output directly to subsequent chain steps.

Yes. Every call to `generate_pdf_from_template` is captured by LangChain's native tracing, showing you the exact JSON payload sent and any rendering errors returned by the server.

Use the `merge_multiple_pdfs` tool. Your agent collects the paths of individual PDFs generated in previous chain steps and passes them as a list to the merge tool to produce a single combined document.

You configure the server using the LangChain MCP adapters package. This exposes the tools directly to your agent's tool-calling loop.

The server handles your rendering payloads within ephemeral V8 sandboxes. Your data never persists, keeping your documents confidential.

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