How to Use the Wave Accounting MCP in LangChain
Build complex, multi-step reasoning pipelines for Wave Accounting actions with LangChain.
Works with every AI agent you already use
…and any MCP-compatible client
Connect Wave Accounting MCP to LangChain
Create your Vinkius account to connect Wave Accounting 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.
Complex customer lifecycle management in LangChain
You can build a chain that first finds a contact using `get_customer_details`, then updates their billing address via `update_customer_profile`. This allows your agent to manage the full customer journey before moving on. Agents use these tools to decide which action comes next. For example, if an invoice is overdue, the chain might call `list_accounting_customers` first to confirm contact status before attempting a reminder.
Invoice generation and approval workflows with LangChain
Need to send out invoices? Your agent handles that. It can create a draft using `create_draft_invoice`, then wait for confirmation from another step in the chain before calling `approve_draft_invoice`. This ensures nobody approves an invoice by accident. The full control means you can also clean up mistakes by invoking `delete_draft_invoice` if the original transaction needs to be scrapped.
Full Wave Accounting data retrieval with LangChain
The MCP Server exposes tools that let your agent read everything it needs. You can list all contacts using `list_accounting_customers`, or retrieve a specific invoice history by running `list_accounting_invoices`. This gives the agent the full context required to make smart decisions. Accessing data is simple: calling `get_invoice_details` pulls every piece of information about one specific bill, keeping your multi-step process grounded in facts.
Set up Wave Accounting MCP in LangChain
Prerequisites
- Python 3.10+ installed
-
langchain-mcp-adapters+langgraphpackages - 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 LangChainBaseToolobjects. - 2
Connect via HTTP transport
Use
MultiServerMCPClientwith"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 Accounting tool calls through the MCP protocol. - 4
Run with any LLM
Swap
ChatOpenAIforChatAnthropic,ChatGoogleGenerativeAI, or any LangChain-compatible model. The MCP tools work identically across all providers.
from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI
async with MultiServerMCPClient({
"wave-accounting-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 Accounting transactions"
})
print(result["messages"][-1].content) Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Wave Accounting. 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 Wave Accounting MCP in LangChain
Use it with your favorite AI tools
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