How to Use the Alegra MCP in LangChain

Q: How do I use Alegra with LangChain to check overdue invoices?

Your agent can call listinvoices with a past-due date filter. Then, for each overdue invoice, it can use getcontactdetails to find the client's email. You build the logic, the agent executes the sequence.

Q: What's the best way to connect Alegra and LangChain?

Use the langchain-mcp-adapters library. You'll instantiate a MultiServerMCPClient with your Vinkius endpoint, get the tools, and pass them to your agent constructor. The whole process takes just a few lines of code.

Q: Does the Alegra MCP Server for LangChain support filtering?

Yes, the listinvoices tool specifically supports date filtering. This lets your agent ask for invoices within a certain time range, which is critical for building financial reports or checking payment statuses.

Build agents that manage your Alegra business data. Connect LangChain to your ERP for multi-step invoice and inventory workflows.

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Connect Alegra MCP to LangChain

Create your Vinkius account to connect Alegra 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 Alegra with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Chain Together Business Operations

Your LangChain agent can now run complex sequences against your Alegra account. It's not just about one-off commands. Build a chain that first uses `list_contacts` to find a client, then runs `list_invoices` with a date filter for that client, and finally calculates a total. This is how you automate real business logic. An agent can check stock with `list_inventory_items`, see if an invoice is paid with `get_invoice_details`, and then `create_contact` for a new supplier, all in one autonomous run. You define the goal, the agent figures out the steps.

Create Invoices and Contacts from Any Source

Hook up Alegra to your other LangChain integrations. Your agent can read an email, extract the details, and then call `create_invoice` to generate a bill in Alegra automatically. No more manual data entry. The same goes for contacts. Feed your agent a spreadsheet or a Slack message, and it can parse the information to run `create_contact`. Because it's a LangChain tool, you can combine it with hundreds of other data sources in a single chain.

Your Alegra MCP Server for Agentic Workflows

This isn't just a simple API wrapper. You get a full set of tools for your ReAct agents to reason with. An agent can start by using `list_estimates` to see open quotes, then check `get_item_details` for product specs before deciding what to do next. You get full observability with LangSmith. Trace every tool call, see the inputs and outputs for `get_contact_details` or `list_payments`, and debug why your agent made a specific decision. This makes building reliable financial agents possible.

Setup guide

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

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Common questions about Alegra MCP in LangChain

Your agent can call `list_invoices` with a past-due date filter. Then, for each overdue invoice, it can use `get_contact_details` to find the client's email. You build the logic, the agent executes the sequence.

No, this MCP server is for managing existing inventory. The agent can check stock levels with `list_inventory_items` and get product info with `get_item_details`, but it can't create new product entries.

Use the `langchain-mcp-adapters` library. You'll instantiate a `MultiServerMCPClient` with your Vinkius endpoint, get the tools, and pass them to your agent constructor. The whole process takes just a few lines of code.

Yes, the `list_invoices` tool specifically supports date filtering. This lets your agent ask for invoices within a certain time range, which is critical for building financial reports or checking payment statuses.

The server only touches the Alegra data your agent explicitly requests. This includes your contacts, invoices, payments, and inventory item data. Vinkius processes these requests in an ephemeral, zero-trust sandbox, and your auth token secures the connection.

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