How to Use the HiFlow MCP in CrewAI

Q: How do I connect HiFlow to my CrewAI setup?

Install crewaitools via pip. You can pass the server URL directly into your agent's mcps array for basic usage. The framework handles the transport negotiation automatically.

Q: Can I restrict which HiFlow tools a specific CrewAI agent can access?

Yes. You import MCPServerHTTP from crewai.mcp and apply a toolfilter. This lets you give the billing agent access to getinvoice while explicitly blocking it from using createcustomer.

Q: Does CrewAI support background monitoring for HiFlow jobs?

It does. You configure a monitor agent to poll listjobs on a schedule. It evaluates the status changes and triggers a response crew only when a project falls behind schedule.

Deploy specialized autonomous agents to manage HiFlow billing and project data using CrewAI.

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Connect HiFlow MCP to CrewAI

Create your Vinkius account to connect HiFlow 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 HiFlow with CrewAI

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Perplexity

CrewAI Agent Specialization

The `list_jobs` and `get_job` tools allow a dedicated Project Manager agent to monitor active workloads. While this agent tracks timelines, it shares memory with an Accounts Receivable agent that simultaneously checks `list_invoices` for overdue payments. This hierarchical execution means you stop writing linear scripts. You define the roles, hand them the tools, and let the crew figure out the exact sequence needed to audit a client's entire project history.

Autonomous Estimate Processing

The `list_estimates` and `get_estimate` tools give your sales crew direct access to quote data. A researcher agent pulls the raw numbers, evaluates the margins, and passes the findings to a moderator agent. That moderator reviews the data against historical averages pulled from `get_activity`. The entire evaluation happens in the background without a human clicking through a dashboard.

Cross-Referencing Timesheets

The `list_timesheets` tool feeds directly into an auditing agent's context. It scans the logged hours and uses `get_me` to verify which user submitted the records. If discrepancies appear, the agent escalates the issue by flagging the specific entries. You get a daily brief of anomalies instead of spending three hours manually matching timesheets to project codes.

Setup guide

Set up HiFlow 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 HiFlow tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

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

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

Install `crewai[tools]` via pip. You can pass the server URL directly into your agent's `mcps` array for basic usage. The framework handles the transport negotiation automatically.

Yes. You import `MCPServerHTTP` from `crewai.mcp` and apply a `tool_filter`. This lets you give the billing agent access to `get_invoice` while explicitly blocking it from using `create_customer`.

It does. You configure a monitor agent to poll `list_jobs` on a schedule. It evaluates the status changes and triggers a response crew only when a project falls behind schedule.

The Python client supports stdio, SSE, and Streamable HTTP. You pick the transport that matches your deployment environment, and the framework manages the underlying connection.

Every environment runs on a V8 Isolate Sandbox managed by Vinkius. When your crew requests `get_estimate`, the server spins up a temporary instance, processes the API call, and destroys the container immediately. Auth is handled server-side, keeping your actual API keys out of the local agent's memory.

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