How to Use the Autenti MCP in LangChain

Q: How do I connect the Autenti MCP Server to LangChain?

Run pip install langchain-mcp-adapters langgraph. Then initialize MultiServerMCPClient with your Vinkius endpoint URL. Grab the tools via client.gettools() and pass them straight into your agent constructor.

Build eIDAS-compliant document signing pipelines right into your LangChain agents using the Autenti MCP Server.

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Connect Autenti MCP to LangChain

Create your Vinkius account to connect Autenti 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 Autenti with LangChain

Ask AI about this MCP

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Claude

Perplexity

LangChain ReAct agents handle e-signatures

Your custom agent needs to fire off contracts based on database triggers. You string together `create_new_signature_process` with your internal CRM tools inside a ReAct loop. The agent pulls metadata from your vector store, drafts the document, and kicks off the signing flow without human intervention. Observability matters when you deal with legal agreements. LangSmith tracks every call to `list_available_process_actions` and `execute_process_action`. You see exactly why an agent decided to send a rejection notice instead of a signature request.

Chain participant lookups

Address books get messy fast. By chaining `list_saved_contacts` with standard LangChain extraction tools, your pipeline normalizes participant data before a contract goes out. The agent validates emails against your internal records. Missing contacts trigger a fallback path. The chain simply executes `add_new_contact` on the fly. Your automated workflow never stalls waiting for someone to manually update a directory.

Monitor contract states in pipelines

Long-running chains need reliable status checks. You can build a background task that polls `list_signature_processes` to find pending agreements. The agent then grabs specific metadata using `get_process_details`. This data feeds directly into your next processing step. Maybe a signed contract triggers a billing event through another API in your LangChain setup. The flow stays entirely automated from draft to final execution.

Setup guide

Set up Autenti 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 Autenti 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({
    "autenti-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 Autenti 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 Autenti. 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 Autenti MCP in LangChain

Run `pip install langchain-mcp-adapters langgraph`. Then initialize `MultiServerMCPClient` with your Vinkius endpoint URL. Grab the tools via `client.get_tools()` and pass them straight into your agent constructor.

Yes, through LangSmith tracing. Every time your agent calls `get_process_details`, you see the exact input tokens, output payload, and latency. Debugging failed signature requests takes seconds.

It handles that easily. Your agent can evaluate missing signers and fire `add_new_contact` dynamically. Just ensure your prompt instructs the agent to check the address book first.

The ReAct agent catches the error and attempts a retry or fallback. If `execute_process_action` rejects a bad parameter, the agent reads the error message and adjusts its payload for the next attempt.

Vinkius isolates your server in a V8 sandbox. Your agent processes sensitive PDF files and eIDAS signatures in an ephemeral environment. We destroy the instance the moment your chain finishes running, leaving zero persistent traces of your legal documents.

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