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Pappers MCP Server for LangChainGive LangChain instant access to 12 tools to Check Api Health, Get Api Account Info, Get Company Document, and more

Built by Vinkius GDPR 12 Tools Framework

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

Ask AI about this App Connector for LangChain

The Pappers app connector for LangChain is a standout in the Industry Titans category — giving your AI agent 12 tools to work with, ready to go from day one.

Vinkius delivers Streamable HTTP and SSE to any MCP client

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({
        "pappers": {
            "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 Pappers, show me what tools are available.",
            }]
        })
        print(response["messages"][-1].content)

asyncio.run(main())
Pappers
Fully ManagedVinkius Servers
60%Token savings
High SecurityEnterprise-grade
IAMAccess control
EU AI ActCompliant
DLPData protection
V8 IsolateSandboxed
Ed25519Audit chain
<40msKill switch
Stream every event to Splunk, Datadog, or your own webhook in real-time

* 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 Pappers MCP Server

Connect your Pappers.fr account to any AI agent and take full control of your French corporate research and business intelligence through natural conversation. Pappers provides the most comprehensive database for French company legal and financial information, and this integration allows you to retrieve detailed profiles (SIREN/SIRET), monitor officer changes, and access official BODACC publications directly from your chat interface.

LangChain's ecosystem of 500+ components combines seamlessly with Pappers through native MCP adapters. Connect 12 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

  • Company Discovery & Search — Search for French businesses programmatically by name, NAF code, or location to ensure your market research is always synchronized.
  • Legal & Compliance Intelligence — Access and monitor officer profiles, ultimate beneficial owners (UBOs), and share capital metadata directly from the AI interface to maintain high-fidelity due diligence.
  • Financial Analysis Control — Retrieve key financial metrics including turnover and net income via natural language to track competitor health or qualify B2B leads.
  • Official Document Oversight — Access legal documents and monitor BODACC publications using simple AI commands to stay informed about corporate events.
  • Operational Monitoring — Track system responses and manage monitored company lists to ensure your business intelligence is always optimized.

The Pappers MCP Server exposes 12 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.

All 12 Pappers tools available for LangChain

When LangChain connects to Pappers through Vinkius, your AI agent gets direct access to every tool listed below — spanning company-data, due-diligence, financial-filings, and more. Every call is secured with network, filesystem, subprocess, and code evaluation entitlements inside a sandboxed runtime. Beyond a simple connection, you get a full AI Gateway with real-time visibility into agent activity, enterprise governance, and optimized token usage.

check_api_health

fr service API. Verify Pappers API status

get_api_account_info

Get Pappers account details

get_company_document

Access legal documents (Articles of Association)

get_company_financials

Get financial data for a company

get_establishment_details

Get details for a specific establishment

get_french_company_details

Get details for a French company

get_search_suggestions

Autocomplete search suggestions

list_bodacc_publications

Search BODACC publications

list_monitored_companies

List companies in your monitoring list

search_company_officers

Search for company directors and managers

search_french_companies

Search for companies in France

search_ultimate_beneficial_owners

Search for UBOs

Connect Pappers to LangChain via MCP

Follow these steps to wire Pappers into LangChain. The entire setup takes under two minutes — your credentials stay safe behind the Vinkius.

01

Install dependencies

Run pip install langchain langchain-mcp-adapters langgraph langchain-openai
02

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token
03

Run the agent

Save the code and run python agent.py
04

Explore tools

The agent discovers 12 tools from Pappers via MCP

Why Use LangChain with the Pappers MCP Server

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

01

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

02

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

03

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

04

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

Pappers + LangChain Use Cases

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

01

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

02

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

03

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

04

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

Example Prompts for Pappers in LangChain

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

01

"Search for companies named 'Vinkius' in France."

02

"Look up the financial details and legal status of the French company with SIREN 443061841."

03

"Search for all companies in the Lyon area that operate in the software development sector."

Troubleshooting Pappers MCP Server with LangChain

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

01

MultiServerMCPClient not found

Install: pip install langchain-mcp-adapters

Pappers + LangChain FAQ

Common questions about integrating Pappers MCP Server with LangChain.

01

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.
02

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.
03

Can I trace MCP tool calls in LangSmith?

Yes. All MCP tool invocations appear as traced steps in LangSmith, showing input parameters, response payloads, latency, and token usage.