Does this tool parse the document directly or use the cloud engine?

The tool offloads the logic specifically via endpoints mapping back to the Parseur Cloud Engine. The AI acts to organize mailboxes, list templates, and fetch final states securely without computing massive local OCR networks.

Can I upload a raw file string to be parsed?

Yes. Utilizing the explicitly mapped `upload_document` constraint, the agent can inject raw string boundaries identifying formatting, passing files straightforward into the target mailbox ID natively.

Will I see missing required fields if extraction fails?

Absolutely. Querying `get_document_details` lists specific status bounds. If a template expects InvoiceTotal and misses it, the document flags a processing boundary issue precisely traceable here.

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

Name: Parseur
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 Parseur through the 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({
        "parseur": {
            "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 Parseur, 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 Parseur MCP Server

Bring Parseur Document Extraction arrays directly into your AI workflows. By explicitly mapping into powerful OCR and templating engines, your agent can push unstructured PDFs or bulk emails into remote routing limits, parsing exact text fields securely. Extract fields, examine documents, list defined parse-templates, and retry pipelines without manual intervention.

LangChain's ecosystem of 500+ components combines seamlessly with Parseur through native MCP adapters. Connect 10 tools via the 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

Mailboxes & Templates — Examine specifically bound mailboxes tracking which explicit templates dictate data extraction limits mapped natively
Document Navigation — Extract properties showing precisely which unstructured strings were identified inside uploaded payloads checking status: parsed correctly
Payload Uploading — Instruct the node limits mapping upload_document generating raw payloads routing straight into the engine for OCR logic
Job Management — Discover disconnected states mitigating failed validations by pushing retry_document instantly forcing physical pipeline resets

The Parseur 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 Parseur to LangChain via MCP

Follow these steps to integrate the Parseur 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 Parseur via MCP

Why Use LangChain with the Parseur MCP Server

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

The largest ecosystem of integrations, chains, and agents — combine Parseur 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 Parseur queries for multi-turn workflows

Parseur + LangChain Use Cases

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

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

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

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

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

Parseur MCP Tools for LangChain (10)

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

create_mailbox

The type determines the parsing engine (e.g., "pdf", "email", "attachment"). Once created, you can configure templates and forward documents to the mailbox for automatic extraction. Create a new Parseur mailbox for document parsing

create_template

Pass the template name and a JSON config string defining field mappings. Parseur will use this template to extract structured data from matching documents. Create a new extraction template for a Parseur mailbox

get_document_data

Fields depend on the template configuration (e.g., invoice_number, total_amount, line_items). Only works for documents with status "processed". Retrieve the fully extracted JSON data from a parsed document

get_document_details

Does not include the parsed data itself — use get_document_data for that. Get metadata of a single parsed document

get_mailbox

Use this to verify mailbox setup before sending documents. Get detailed configuration of a specific Parseur mailbox

list_documents

Each entry includes document ID, status (processed, failed, pending), and metadata like sender and received date. List all parsed documents inside a Parseur mailbox

list_mailboxes

Each mailbox represents a parsing pipeline for a specific document type (invoices, receipts, emails). Use the returned mailbox IDs for subsequent operations like listing documents or uploading files. List all Parseur parsing mailboxes

list_templates

Templates define the extraction rules (field names, locations, regex patterns) used to pull structured data from incoming documents. List available extraction templates for a Parseur mailbox

retry_document

Useful after fixing template rules or when the original parse failed due to a transient error. The document will be matched against the latest template rules. Retry parsing a failed or errored Parseur document

upload_document

eml) to the specified mailbox for automatic parsing. The document enters the processing queue and will be parsed according to the mailbox template. Returns the new document ID for tracking. Upload a document URL to a Parseur mailbox for parsing

Example Prompts for Parseur in LangChain

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

"Check my Parseur mailboxes to find the specific bounding IDs."

"Get the data schema parsed tightly inside document doc_987."

"Upload this snippet of parsed text directly into Mailbox xyz12 for OCR processing."

Troubleshooting Parseur MCP Server with LangChain

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

MultiServerMCPClient not found

Install: pip install langchain-mcp-adapters

Parseur + LangChain FAQ

Common questions about integrating Parseur 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.