How to Use the Holded MCP in LangChain

Q: Can my LangChain agent read my Holded invoices?

Yes, it uses listinvoices to pull data into the chain. Your agent then processes the invoice list to generate reports or summaries.

Q: Does Holded support LangChain state management?

The server remains stateless by default. Use client.session() in your code if you need to keep context across multiple chain executions.

Chain your Holded data into automated business logic with LangChain. Build agents that invoice, track inventory, and manage CRM.

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Connect Holded MCP to LangChain

Create your Vinkius account to connect Holded 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 Holded with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Automate Holded workflows in LangChain chains

Pipe business data directly into your agent logic. You define a chain that triggers `create_invoice` based on data from `list_sales_orders` without manual data entry. Your agent inspects the output of `list_products` to decide whether to replenish stock. Every step is logged in your tracing environment.

Agentic control over Holded CRM and accounting

Give your LangChain agent the ability to query `list_contacts` to find client details for your communications. It handles the request and parses the JSON response. Decision-making happens in the loop. The agent evaluates the output of `list_payments` to determine the next action in your custom pipeline.

Multi-server aggregation for business intelligence

Combine this MCP server with other data sources in your LangChain graph. You can compare data from `list_expenses` against external databases to run cost analysis. This setup keeps your logic modular. You swap tools or add new integrations without rewriting your entire agent architecture.

Setup guide

Set up Holded 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 Holded 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({
    "holded-alternative-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 Holded 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 Holded. 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 Holded MCP in LangChain

Install the MCP adapters and point your client to the Vinkius endpoint. Use the `MultiServerMCPClient` to load the tools into your agent's execution context.

Yes, it uses `list_invoices` to pull data into the chain. Your agent then processes the invoice list to generate reports or summaries.

The server remains stateless by default. Use `client.session()` in your code if you need to keep context across multiple chain executions.

Your data is scoped to your session and handled through the secure Vinkius sandbox. Only your specific agent instance accesses your financial records.

The MCP schema enforces parameter requirements for tools like `create_invoice`. If the agent misses a required field, the server returns an immediate error.

Use it with your favorite AI tools

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

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