How to Use the FlowiseAI MCP in CrewAI

Q: How can I filter FlowiseAI tools for specific CrewAI agents?

Use the toolfilter argument in your MCP server setup. This allows you to restrict which agents have access to sensitive tools like credential management.

Q: Can CrewAI agents share memory across FlowiseAI chatflows?

Yes, by storing state in variables accessed via listflowvariables. Your agents can read and write to these variables to coordinate their actions.

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Connect FlowiseAI MCP to CrewAI

Create your Vinkius account to connect FlowiseAI 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 FlowiseAI with CrewAI

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Perplexity

Autonomous crew orchestration

Assign agents to trigger `execute_chatflow_prediction` based on their specific roles. A researcher agent can fetch data while a writer agent processes the output. This creates a division of labor where the MCP server acts as the central engine. Your agents collaborate on the tasks while the server provides the logic.

Feedback-driven agent refinement

Use `list_chat_feedback` to have an analyst agent review past interactions. If feedback is negative, the agent can adjust the prompt templates via the orchestration layer. This loop turns your agents into self-improving units. The server provides the raw data, and your crew handles the optimization.

Template-based agent tasks

Query `list_marketplace_templates` to let your agents discover new capabilities. They can identify the right flow for a specific request and execute it automatically. Your crew stays flexible by adapting to new templates. You define the high-level goals, and the agents map them to available chatflows.

Setup guide

Set up FlowiseAI 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 FlowiseAI tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

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

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

Use the tool_filter argument in your MCP server setup. This allows you to restrict which agents have access to sensitive tools like credential management.

Yes, by storing state in variables accessed via `list_flow_variables`. Your agents can read and write to these variables to coordinate their actions.

Provide the required endpoint token in your MCP configuration. The server will use this for all tool calls made by your agent crew.

The server operates under your strict configuration. You should monitor the agent logs to verify the actions being performed by your crew.

The server accesses your chat history and lead logs. These are sensitive operational records that are isolated within your local environment.

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