How to Use the Certify (Emburse) MCP in LangChain

Q: How do I pass Certify (Emburse) tools to a LangChain agent?

Install the langchain-mcp-adapters package and initialize the MultiServerMCPClient. Call client.gettools() to fetch the Certify tools and pass them directly to your agent's constructor.

Q: Can a LangChain agent use Certify (Emburse) to find receipts?

Yes, the agent calls listcertifyreceipts to fetch stored receipts. You can chain this with expense data to verify that every claimed expense has an accompanying receipt image.

Q: How does LangSmith help debug Certify (Emburse) LangChain runs?

LangSmith logs every call to tools like listexpensereports or listinvoicereports. You see the exact inputs, outputs, latency, and token usage for every single transaction query.

Run multi-step expense reconciliation chains in LangChain using direct Certify (Emburse) data.

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Connect Certify (Emburse) MCP to LangChain

Create your Vinkius account to connect Certify (Emburse) 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 Certify (Emburse) with LangChain

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Build LangChain audit agents with the Certify MCP Server

This MCP server exposes `list_certify_expenses` and `list_expense_reports` to feed raw transaction data directly into your LangChain runnables. Your agent pulls active reports, parses the individual lines, and flags policy violations without manual data entry. You chain these tools together so the output of one step feeds the next. For example, the agent gets a list of reports, extracts the IDs, and then checks individual expense details to find outliers in seconds.

Auto-match invoices to GL codes in LangChain

This MCP server provides `list_certify_invoices` and `get_gl_dimensions` to let your agent map accounts payable records to your ledger. The agent inspects pending invoices and matches them against your existing general ledger dimensions in a single chain step. LangSmith traces every tool call, showing you exactly how the agent mapped each invoice to a specific GL category. You get full visibility into the decision-making path before committing the data.

Validate department spend per user

This MCP server connects `list_certify_users` and `list_certify_departments` directly to your LangChain chains. Your agent queries the active employee directory and matches users to their respective department codes during spend analysis. This setup lets you build automated workflows that flag when a user submits an expense under an incorrect department code. It replaces manual cross-referencing by letting the agent do the lookup in real time.

Setup guide

Set up Certify (Emburse) 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 Certify (Emburse) 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({
    "certify-emburse-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 Certify (Emburse) 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 Certify. 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 Certify (Emburse) MCP in LangChain

Install the `langchain-mcp-adapters` package and initialize the `MultiServerMCPClient`. Call `client.get_tools()` to fetch the Certify tools and pass them directly to your agent's constructor.

Yes, the agent calls `list_certify_receipts` to fetch stored receipts. You can chain this with expense data to verify that every claimed expense has an accompanying receipt image.

LangSmith logs every call to tools like `list_expense_reports` or `list_invoice_reports`. You see the exact inputs, outputs, latency, and token usage for every single transaction query.

Yes. The connection is stateless by default, but you can use `client.session()` if your agent needs to maintain context across multiple sequential Certify queries.

All credentials for accessing your invoices, receipts, and employee lists are managed in Vinkius's secure, zero-trust sandbox. The LangChain client only receives the final tool outputs over an encrypted transport layer, keeping your raw financial records isolated.

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