How to Use the Wave Financial MCP in LangChain

Q: If I need to check products sold in a specific business, how do I use LangChain?

You'll call listproducts and pass the required business ID. The resulting list gives you all available items or services for that location. You can then chain this output with other tools like listinvoices to see where those products were actually sold.

Run complex financial processes in chains using LangChain.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect Wave Financial MCP to LangChain

Create your Vinkius account to connect Wave Financial 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.

GDPR Free for Subscribers

Setup Wave Financial with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Multi-Step Financial Reporting

You build a pipeline that calls several tools sequentially. For example, your agent first runs `list_businesses` to identify all entities. It then takes the resulting list and feeds it into `list_accounts`, generating an aggregated financial snapshot for every location. This chaining ability lets you model real-world workflows. Your agent can automatically check if a business exists before trying to pull its invoices or vendors, preventing dead ends in your code.

Accountability and Vendor Management

Need to reconcile accounts payable across multiple businesses? Start by calling `list_vendors` for each location. Then, the chain can iterate through those vendors' open bills using `list_bills`. This gives you a full picture of who owes money and why. It’s powerful because the output from one tool—say, a vendor ID—becomes the required input for the next tool call (`list_bills`). You don't write complex loops; your agent handles the orchestration.

Comprehensive Customer Data Retrieval

Gathering full customer history is straightforward. An agent can first use `list_customers` to get a list of clients for a business. Next, it pulls all associated invoices using `list_invoices`. Finally, you can check the payment status by examining transactions via `list_transactions`. This sequence allows your agent to build a 360-degree view of any client's relationship with Wave Financial. You’re building deep reasoning logic here.

Setup guide

Set up Wave Financial 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 Wave Financial 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({
    "wave-financial-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 Wave Financial transactions"
    })
    print(result["messages"][-1].content)

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Wave Financial. 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.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

Connect Wave Financial now

Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about Wave Financial MCP in LangChain

LangChain lets you build multi-step chains that access the Wave Financial MCP Server. You don't just run a single query; you connect tools like `list_accounts` and `list_invoices` into an operational pipeline. This means your agent can perform complex tasks, not just simple lookups.

Yes. Because the server supports listing all associated businesses via `list_businesses`, you can write a chain that iterates through every single legal entity attached to your account, ensuring no financial gap is missed.

This MCP Server handles sensitive business data including user profiles (`get_user_info`), billing records (`list_invoices`), and operational accounts. When building with LangChain, always treat the output of these tools as confidential.

You'll call `list_products` and pass the required business ID. The resulting list gives you all available items or services for that location. You can then chain this output with other tools like `list_invoices` to see where those products were actually sold.

The best way is to define clear, sequential steps. Start by identifying the scope (e.g., running `list_businesses`) and then use that output as a filter for your core financial tools like `list_transactions`.

Use it with your favorite AI tools

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

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript