How to Use the Uniconta MCP in LangChain

Q: Can I use Uniconta's inventory list in a LangChain agent?

Absolutely. You can start by running listinventoryitems to get all product SKUs. Then, you can chain that data with other lookups—like checking which employees (listemployees) are authorized to approve those products—to build complex approval workflows.

Q: Does Uniconta support multi-step financial analysis using LangChain?

Yes. You can combine listglaccounts with listdailyjournals. Your agent doesn't just pull a list; it processes the sequence, allowing you to build reports that summarize transactions across different accounts.

Q: What if I need to check multiple customer records in Uniconta with LangChain?

You can write a chain that first calls listdebtors for a list of IDs. Then, the agent iterates through those results, calling getdebtordetails sequentially for every single record, giving you bulk data processing.

Build Multi-Step ERP Logic with LangChain. Connect any Uniconta data point into your processing chains.

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Connect Uniconta MCP to LangChain

Create your Vinkius account to connect Uniconta 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 Uniconta with LangChain

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Building multi-step reasoning pipelines

The `list_debtors` tool lets your agent pull a full roster of all registered customers in the ERP. If you need to check their outstanding balances, the agent can follow up by calling `get_debtor_details`, creating a smooth data flow for complex checks. This chaining capability means the output from one call—like a customer ID—becomes the direct input for the next tool. Your AI client runs multi-step logic without needing intermediate human intervention.

Inventory and Vendor Management

Need to know what products you carry? Use `list_inventory_items` to get a catalog of every available item in Uniconta. You can then cross-reference those items against vendor records by calling `list_creditors` to ensure all suppliers are accounted for. This process lets your agent verify that the right supplies match the correct vendors before initiating an order. It handles these lookups automatically within a single, traceable chain.

General Ledger and Financial Queries

Start by getting a list of all available GL codes using `list_gl_accounts`. This is the foundational step for any financial analysis your agent runs. From there, you can narrow down history by listing daily journals with `list_daily_journals`. Your client builds complex reports like this. It chains together account lists and journal summaries to give you a full picture of financial activity in Uniconta.

Setup guide

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

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Common questions about Uniconta MCP in LangChain

LangChain treats every MCP tool call as a step in a pipeline. If your agent gets debtor details using `get_debtor_details`, it can then automatically pass that ID to check the customer’s invoices via `list_debtor_invoices`. The data moves naturally from one function to the next.

Absolutely. You can start by running `list_inventory_items` to get all product SKUs. Then, you can chain that data with other lookups—like checking which employees (`list_employees`) are authorized to approve those products—to build complex approval workflows.

Yes. You can combine `list_gl_accounts` with `list_daily_journals`. Your agent doesn't just pull a list; it processes the sequence, allowing you to build reports that summarize transactions across different accounts.

You can write a chain that first calls `list_debtors` for a list of IDs. Then, the agent iterates through those results, calling `get_debtor_details` sequentially for every single record, giving you bulk data processing.

This server handles core financial and operational records. Specifically, it touches Account Records (from `list_gl_accounts`), Customer Data (from `list_debtors`), and Invoice Records (from `list_debtor_invoices`).

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