How to Use the Certify (Emburse) MCP in CrewAI

Deploy an autonomous finance team for Certify with CrewAI. Agents collaborate to monitor expenses, audit reports, and manage invoices.

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Connect Certify (Emburse) MCP to CrewAI

Create your Vinkius account to connect Certify (Emburse) to CrewAI 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 CrewAI

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Deploy a Finance Audit Crew

With CrewAI, you can build a team of agents to manage expense compliance. The 'Monitor' agent runs hourly, using `list_expense_reports` to find new submissions. It passes new reports to the 'Auditor' agent. The Auditor's job is to use `list_certify_expenses` and `list_certify_receipts` to check for policy violations, like missing receipts. If it finds an issue, it tasks the 'Notifier' agent to alert the right manager. The whole process runs on its own.

Automate Accounts Payable

Set up a dedicated AP crew. An 'Intake' agent's sole purpose is to `list_certify_invoices` and add new ones to a shared task list. A second 'Analyst' agent picks up those tasks, using `get_gl_dimensions` to categorize each invoice correctly. A 'Reporting' agent can then run at the end of the day, using `list_invoice_reports` to generate a summary for the CFO. This isn't just one agent running a script; it's a team of specialized agents collaborating.

The Certify MCP Server for CrewAI

This MCP server provides the specific financial tools your CrewAI agents need to function. You can create a 'Compliance' agent and use CrewAI's `tool_filter` to give it access only to `list_certify_users` and `list_certify_departments`. Meanwhile, your 'AP' agent gets the invoice and reporting tools. This lets you build a secure, role-based system where each agent has the minimum permissions necessary to do its job. It's the right way to build autonomous financial operations with this MCP integration.

Setup guide

Set up Certify (Emburse) MCP in CrewAI

Prerequisites

Python 3.10+ installed
crewai package (pip install crewai)
Active Vinkius subscription with a valid endpoint token

1

Install CrewAI
Run pip install crewai to install the framework. MCP support is built-in via the mcps parameter.
2

Add the MCP URL to your agent
Pass your Vinkius endpoint directly to the mcps list. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. CrewAI handles tool discovery and caching automatically.
3

Kick off your crew
Create a Crew with your agent and tasks. Call crew.kickoff() — the agent will automatically invoke Certify (Emburse) tools as needed.

crew.py

from crewai import Agent, Task, Crew

agent = Agent(
    role="Certify (Emburse) Analyst",
    goal="Access and analyze Certify (Emburse) data via MCP.",
    backstory="Expert analyst with direct Certify (Emburse) access.",
    mcps=[
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ],
)

task = Task(
    description="List recent Certify (Emburse) transactions",
    agent=agent,
    expected_output="A summary of recent activity",
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

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Prerequisites

Python 3.10+ installed
crewai + crewai-tools packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install crewai crewai-tools. The MCPServerAdapter handles lifecycle management and tool conversion.
2

Connect with MCPServerAdapter
Use MCPServerAdapter as a context manager with SseServerParameters pointing to your Vinkius endpoint. The adapter automatically manages connection lifecycle.
3

Assign tools and run
Pass the returned mcp_tools to your agent's tools parameter. The adapter converts MCP tools to native BaseTool objects compatible with all CrewAI agents.

crew.py

from crewai import Agent, Task, Crew
from crewai_tools import MCPServerAdapter
from mcp import SseServerParameters

server_params = SseServerParameters(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

with MCPServerAdapter(server_params) as mcp_tools:
    agent = Agent(
        role="Certify (Emburse) Analyst",
        goal="Access and analyze Certify (Emburse) data via MCP.",
        backstory="Expert analyst with direct Certify (Emburse) access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent Certify (Emburse) transactions",
        agent=agent,
        expected_output="A summary of recent activity",
    )

    crew = Crew(agents=[agent], tasks=[task])
    result = crew.kickoff()
    print(result)

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 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 CrewAI

Define different agent roles in your crew, like an 'Auditor' and a 'Reporter'. Assign tools from the Certify (Emburse) MCP integration to each agent based on their role. For example, the Auditor gets `list_expense_reports` and the Reporter gets `list_invoice_reports`.

Yes. When you define your agents, you can pass a specific list of tools to each one. This means your 'Invoice' agent can't accidentally access employee lists, giving you fine-grained control over your Certify (Emburse) data.

A good start is a two-agent crew. Agent one's task is to periodically check for new invoices with `list_certify_invoices`. Agent two's task is to process them, using `get_gl_dimensions` to add accounting codes and flagging any for review.

Yes. CrewAI itself focuses on defining the agents and tasks. You would use an external scheduler, like a cron job or a cloud function, to trigger your crew's `kickoff()` method at whatever interval you need.

Security is handled by the Vinkius platform. Your connection to the MCP server is authenticated, and each request is processed in an ephemeral, zero-trust sandbox. The system only processes the data required for a given tool, like GL dimensions or user lists, and doesn't store anything permanently.

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