How to Use the Fellow MCP in CrewAI

Deploy specialized agent crews to manage meeting notes and action items using the Fellow MCP Server.

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…and any MCP-compatible client

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Connect Fellow MCP to CrewAI

Create your Vinkius account to connect Fellow 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 Fellow with CrewAI

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Perplexity

Collaborative agent crews

Assign specific roles to your agents for meeting management. One agent can use `list_notes` to summarize discussions, while another uses `get_action_item` to assign follow-ups. This division of labor ensures that every meeting note is processed and every action item is tracked by the right expert agent.

Autonomous task lifecycle management

Let your crew handle the administrative heavy lifting. Agents can verify task completion using `complete_action_item` or archive outdated entries with `archive_action_item` without human intervention. This keeps your workspace clean and ensures that only active tasks remain in your primary view.

Transcript analysis for CrewAI

Process long-form meeting data with dedicated analysis agents. By providing `get_transcript` to your crew, they can identify key decisions and extract project requirements automatically. This turns hours of audio into structured data that informs your crew's next moves.

Setup guide

Set up Fellow 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 Fellow tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

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

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

By providing a standardized interface for meeting data, Fellow allows your agents to share a common memory. Your crew can query `list_notes` to stay aligned on project goals.

Yes, once an agent determines a task is done, it can invoke `complete_action_item` to update Fellow. This keeps your team accountability high without manual updates.

You can use tool filters in your agent definition to limit access. For example, give your researcher agent access to `get_transcript` but restrict your admin agent to `archive_action_item`.

You should configure your agents to require human approval for any destructive commands. Always test your agent logic before granting access to `delete_note` or `delete_recording`.

Your data is scoped to your specific API token. The server limits access to the tools, ensuring that only the agents in your crew can access your meeting transcripts and action item lists.

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