How to Use the Evoliz MCP in CrewAI

Q: How do I configure CrewAI to use the Evoliz MCP server?

Run pip install crewai "crewaitools". You can pass the MCP server endpoint URL directly into the mcps array on your agent definition.

Q: Can I restrict which Evoliz tools my CrewAI agents can use?

Yes. Use the MCPServerHTTP class from crewai.mcp and configure a tool filter. This prevents a basic research agent from calling createclient.

Build autonomous multi-agent accounting teams in CrewAI using the Evoliz MCP server. Automate French tax compliance and invoicing.

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Connect Evoliz MCP to CrewAI

Create your Vinkius account to connect Evoliz 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 Evoliz with CrewAI

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Deploy Autonomous Accounting Crews

Build a data entry agent that runs `create_client` while a separate auditor agent monitors the output for compliance. CrewAI lets you assign specific tasks to specialized agents. These agents share memory and pass context back and forth. The auditor agent can pull historical records using `list_invoices` to ensure the new client matches your existing billing patterns.

Filter Access by Agent Role

Using the MCP server HTTP class in CrewAI, you can apply a tool filter to restrict what each agent can do with `list_articles` and `list_quotes`. Not every agent needs access to your entire financial history. You might give a sales agent access to draft proposals. Meanwhile, only the senior finance agent gets permission to read sensitive data via `get_invoice`.

Reconcile Billing Discrepancies

Set up a hierarchical execution crew where a junior agent fetches raw data using `list_clients` and `list_quotes`. You can reconcile billing discrepancies automatically. The manager agent reviews the findings. If it spots a missing payment, it triggers `get_client` to pull the contact details and drafts an escalation email, entirely without human intervention.

Setup guide

Set up Evoliz 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 Evoliz tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

task = Task(
    description="List recent Evoliz 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 →

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="Evoliz Analyst",
        goal="Access and analyze Evoliz data via MCP.",
        backstory="Expert analyst with direct Evoliz access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent Evoliz 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 Evoliz. 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 Evoliz MCP in CrewAI

Run `pip install crewai "crewai[tools]"`. You can pass the MCP server endpoint URL directly into the `mcps` array on your agent definition.

Yes. Use the `MCPServerHTTP` class from `crewai.mcp` and configure a tool filter. This prevents a basic research agent from calling `create_client`.

CrewAI agents operate with shared memory. If one agent runs `get_invoice`, the resulting data becomes available to the rest of the crew for subsequent analysis.

The framework supports stdio, SSE, and Streamable HTTP. CrewAI connects directly to the Vinkius managed endpoint using standard HTTP protocols.

Your client names and business types are highly protected. The Vinkius infrastructure runs your `list_clients` requests inside a zero-trust, ephemeral environment that vanishes the moment the execution completes.

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