How to Use the FlowUs MCP in CrewAI

Q: How do I limit which FlowUs tools a CrewAI agent can use?

Use the MCPServerHTTP class from crewai.mcp to filter the MCP Server tools. This lets you restrict an agent to reading pages with getpage while blocking write tools.

Deploy autonomous CrewAI agent teams to research, draft, and publish structured documentation directly to your FlowUs workspace.

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Connect FlowUs MCP to CrewAI

Create your Vinkius account to connect FlowUs 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 FlowUs with CrewAI

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Deploy Autonomous Document Crews using this MCP Server

Stop running single prompts and start running collaborative teams. With CrewAI, you can assign one agent to analyze documentation via `list_pages` and another to write updates using `update_page`. The agents share context and memory, ensuring that a researcher agent's findings are instantly available to the writer agent when writing new documents with `create_page`.

Multi-Agent Database Moderation and Auditing

Keep your structured data clean with a specialized crew. A monitoring agent can search for anomalies using `query_database`, while an editor agent corrects errors by executing `create_database_row`. This hierarchical execution prevents bad data from slipping through. The agents inspect schemas using `get_database` to ensure every entry matches your workspace standards before writing.

Automated Team Directory and Access Management

Managing workspace access doesn't have to be a manual chore. Your CrewAI crew can audit active accounts by running `list_users` and compare them against your internal team directories. When changes are detected, the crew can update documentation blocks using `list_blocks` to keep your team rosters up to date across all internal wiki pages.

Setup guide

Set up FlowUs 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 FlowUs tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

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

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

You can pass the Vinkius HTTP endpoint directly into the agent's `mcps` parameter during initialization. The agent automatically discovers tools like `list_pages` and starts using them.

Yes, CrewAI supports shared memory. One agent can query the table using `query_database` and hand the results to a second agent to write updates using `create_database_row`.

Use the `MCPServerHTTP` class from `crewai.mcp` to filter the MCP Server tools. This lets you restrict an agent to reading pages with `get_page` while blocking write tools.

Yes, you can define tasks sequentially. The first task can gather data from `list_blocks`, and the second task can use that data to run `update_page` across your workspace.

Yes, your page content, database rows, and user directories remain completely private. All operations go through our secure MCP Server sandbox. Your sensitive data is processed in memory and never stored or used to train public models.

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