How to Use the Airparser MCP in LangChain

Q: How do I connect the Airparser MCP Server to my LangChain agent?

Install langchain-mcp-adapters and use MultiServerMCPClient to point to the Vinkius endpoint. This exposes all ten parsing tools directly to your agent's tool list.

Q: Can LangChain handle async document processing with Airparser?

Yes. Your agent can trigger parsedocumentasync for large PDFs and then use listdocuments to check the status. This keeps your main LangChain loop from blocking on slow files.

Q: How does LangChain manage schema validation with Airparser?

Your chain calls getinboxschema to fetch the expected JSON structure first. The agent then matches the output of getdocumentdetails against this schema to ensure data integrity.

Feed parsed PDF and email data directly into your LangChain reasoning chains with this managed MCP Server.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect Airparser MCP to LangChain

Create your Vinkius account to connect Airparser 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.

GDPR Free for Subscribers

Setup Airparser with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Convert raw documents into structured JSON

`parse_document_sync` extracts clean data from your PDFs, images, and emails instantly. Your LangChain agent passes the file, extracts the schema using `get_inbox_schema`, and feeds the clean JSON directly into the next chain link. No more manually writing parser code or cleaning messy text before your LLM can read it. You get pure, structured outputs that match your exact database schema without writing custom extraction scripts.

Build self-triggering document workflows

`create_webhook` lets your LangChain pipeline register automated export targets. By tracking events with `list_webhooks`, your agent manages the flow of incoming documents without constant manual polling. This stops your system from wasting API credits on empty checks. Your agent sets up the receiver, listens for parsed documents, and runs downstream chains only when new data arrives.

Trace Airparser MCP Server calls in LangSmith

`get_document_details` pulls specific extraction results directly into your active LangChain execution graph. Because every tool call runs through the standard protocol, LangSmith captures the exact latency and token cost of your document extraction steps. You see precisely how long `parse_document_async` takes to process large files. Debugging failed extractions becomes a matter of checking the trace, not digging through raw HTTP logs.

Setup guide

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

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Airparser. 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.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

Connect Airparser now

Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about Airparser MCP in LangChain

Install `langchain-mcp-adapters` and use `MultiServerMCPClient` to point to the Vinkius endpoint. This exposes all ten parsing tools directly to your agent's tool list.

Yes. Your agent can trigger `parse_document_async` for large PDFs and then use `list_documents` to check the status. This keeps your main LangChain loop from blocking on slow files.

Your chain calls `get_inbox_schema` to fetch the expected JSON structure first. The agent then matches the output of `get_document_details` against this schema to ensure data integrity.

The tool returns an error state which your agent catches. You can build a fallback chain that routes the failed document to a manual review queue.

Vinkius runs this server inside an isolated, zero-trust sandbox. Your parsed PDFs, emails, images, and inbox metadata are processed ephemerally and never stored on our hosting layer.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript