How to Use the Legito MCP in LangChain

Q: Can I use Legito and LangChain for automated auditing?

Absolutely. You can chain listusers and getuserpermissions to generate audit reports. It provides a clear view of who accessed what.

Chain Legito document actions directly into your LangChain workflows for automated lifecycle management.

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Connect Legito MCP to LangChain

Create your Vinkius account to connect Legito 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 Legito with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Automate document records in LangChain

Pipe `list_document_records` outputs directly into your agent logic. You get real-time visibility into your document status without leaving your execution chain. Then use `update_document_record` to push changes based on agent reasoning. It turns static files into dynamic assets within your pipeline.

Control access through MCP Server pipelines

Integrate `share_with_user` and `update_user_permissions` to gate access programmatically. Your agent evaluates the context and updates rights based on current policy. This MCP Server lets you chain these calls after any validation step. You define the logic, and the agent enforces it.

Manage templates with structured chains

Invoke `list_template_suites` to fetch your inventory for document generation. It populates your agent's context with the right schemas immediately. Follow up by calling `create_document_version_data` to generate new versions. Every step remains traceable through your standard observability tools.

Setup guide

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

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Common questions about Legito MCP in LangChain

You instantiate the client and pass the tools to your agent. Each `list_document_records` call returns data that your agent uses to decide the next action.

Yes. Use the client session to maintain context across your chain. This ensures `update_document_version_data` operations have access to previous state.

Absolutely. You can chain `list_users` and `get_user_permissions` to generate audit reports. It provides a clear view of who accessed what.

It acts as a bridge for your agent. You manage the session, and the server handles the raw API requests.

We enforce strict transport security for every request. All document records remain within your authorized workspace, and no data leaves your controlled environment.

Use it with your favorite AI tools

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

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