How to Use the Cacheflow MCP in CrewAI

Q: Can I filter which Cacheflow tools my CrewAI agents see?

Use the toolfilter option to limit access. This ensures your research agents can list data but can't accidentally create new proposals.

Deploy a specialized crew of agents in CrewAI using Cacheflow to manage your entire sales pipeline autonomously.

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Connect Cacheflow MCP to CrewAI

Create your Vinkius account to connect Cacheflow 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 Cacheflow with CrewAI

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Perplexity

CrewAI agents specializing in Cacheflow tasks

Assign a research agent to call `list_customers` and a finance agent to run `get_approval_requests`. They share memory to keep the deal context consistent across the crew. This setup allows for complex sequences. One agent prepares the quote, while the other verifies it against your CRM records.

Hierarchical execution with Cacheflow and CrewAI

Set a manager agent to oversee the `create_proposal` process. It delegates the work to subordinates and checks the output before finalizing the task. It creates a reliable chain of command. You get a finished, validated proposal without having to manage the individual steps yourself.

Monitoring sales cycles via CrewAI and Cacheflow

Use a monitor agent to periodically run `list_proposals` and alert you to expiring deals. It keeps a pulse on your pipeline while you focus on other work. It acts as an automated assistant that never sleeps. You get summarized updates on the state of your sales activity.

Setup guide

Set up Cacheflow 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 Cacheflow tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

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

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

Pass the server URL in your agent definition under the mcps list. The framework handles the connection automatically.

Use the tool_filter option to limit access. This ensures your research agents can list data but can't accidentally create new proposals.

You can add multiple MCP endpoints to your agents. Each agent can interact with different services to complete a multi-step workflow.

Agents share context through the crew's memory. Once one agent retrieves proposal details, the other agents can use that information for their next task.

Your information is handled through secure ephemeral sessions. We ensure that only authorized agents access the tools, and no sensitive customer records are cached outside of your active run.

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