How to Use the Ziflow MCP in CrewAI

Q: How do I search proofs with Ziflow and CrewAI?

One agent calls searchproofs to gather data. A second agent analyzes the results, determining which subset of proofs requires immediate action or review.

Q: Can my crew list all webhooks using Ziflow and CrewAI?

Yes. The 'Integration Manager' agent can run listwebhooks to gather the full scope of active integrations, which is then passed to a 'Report Generator' for analysis.

Q: How does Ziflow handle proof metadata with CrewAI?

The crew uses listintegrationproperties to inspect deep-level data. This lets the agents validate that all necessary content requirements are met before proceeding with a major workflow step.

Q: Does this MCP Server provide account information for Ziflow and CrewAI?

Yes, getaccountinfo pulls the profile data. This is vital for agents that need to validate permissions or context before taking action.

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Create your Vinkius account to connect Ziflow 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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Coordinate Proof Creation Across Agents

The `create_proof` tool allows one agent to initiate a new proof, while another specialized agent might use `list_folders` to find the right destination. This mimics a real-world collaborative review process. The overall crew maintains shared memory of this creation step, ensuring all subsequent actions are context-aware.

Verify User Access and Roles

A dedicated agent can run `list_team_users` to gather an inventory of who has access. Another agent uses `get_account_info` to verify specific user roles against the platform's current state. This sequence allows your crew to autonomously audit permissions and report discrepancies.

Manage Content Review Links

If an agent finds a proof, it can use `get_proof_viewer_url` to generate the necessary review link. A follow-up agent can then send this link via messages or records. This hands off the final action—sharing—to another specialized tool within your autonomous workflow.

Setup guide

Set up Ziflow 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 Ziflow tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

task = Task(
    description="List recent Ziflow 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="Ziflow Analyst",
        goal="Access and analyze Ziflow data via MCP.",
        backstory="Expert analyst with direct Ziflow access.",
        tools=mcp_tools,
    )

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

One agent calls `search_proofs` to gather data. A second agent analyzes the results, determining which subset of proofs requires immediate action or review.

Yes. The 'Integration Manager' agent can run `list_webhooks` to gather the full scope of active integrations, which is then passed to a 'Report Generator' for analysis.

The crew uses `list_integration_properties` to inspect deep-level data. This lets the agents validate that all necessary content requirements are met before proceeding with a major workflow step.

Yes, `get_account_info` pulls the profile data. This is vital for agents that need to validate permissions or context before taking action.

The server touches proof metadata and status records. You can read details using `get_proof` or the associated properties list.

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