How to Use the Trello MCP in CrewAI

Q: How do I ensure Trello cards are tagged correctly using CrewAI?

An agent first uses listlabels to get all available label IDs. Then, another agent runs addlabeltocard, passing the specific ID and card context to guarantee accurate labeling.

Q: Can I use CrewAI to search for Trello cards by keyword across a board?

Yep. An agent calls searchcards with the necessary keywords and board context. This returns matching cards, which other agents can then analyze or act upon.

Q: What tools does CrewAI use to get all lists (columns) on a Trello board?

The listlists tool pulls the list names from the board. This provides the structural context that allows other agents to know where to look for cards.

Q: How does CrewAI handle card creation and updates in a multi-agent process?

One agent uses createcard when research is complete, or another agent uses updatecard if the task requires modification. The shared memory keeps track of these changes.

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Autonomous Card Management via CrewAI

A card needs updating? Use `update_card` within an agent's task. This allows a specialized agent to modify existing cards based on its research findings, making the change directly in Trello. A specialized agent can also create new tasks using `create_card`. It's perfect for when one agent researches and another takes action by generating a card.

Board Visibility with CrewAI

To get a total board overview, the crew uses `list_board_cards` to collect all cards from every list. This data is shared memory for the whole team of agents to analyze. If you just need to know which boards exist, one agent can run `list_boards`. It provides the initial scope that guides the rest of the autonomous operation.

Searching and Labeling Trello Cards with CrewAI

A card needs a tag? An agent runs `add_label_to_card` after another agent used `list_labels` to confirm the correct ID. This is how role-based specialization works in practice. The crew can locate specific tasks by having an agent run `search_cards`, passing keywords and board context for targeted retrieval.

Setup guide

Set up Trello 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 Trello tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

task = Task(
    description="List recent Trello transactions",
    agent=agent,
    expected_output="A summary of recent activity",
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

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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="Trello Analyst",
        goal="Access and analyze Trello data via MCP.",
        backstory="Expert analyst with direct Trello access.",
        tools=mcp_tools,
    )

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

An agent first uses `list_labels` to get all available label IDs. Then, another agent runs `add_label_to_card`, passing the specific ID and card context to guarantee accurate labeling.

Yep. An agent calls `search_cards` with the necessary keywords and board context. This returns matching cards, which other agents can then analyze or act upon.

The `list_lists` tool pulls the list names from the board. This provides the structural context that allows other agents to know where to look for cards.

One agent uses `create_card` when research is complete, or another agent uses `update_card` if the task requires modification. The shared memory keeps track of these changes.

This server processes board structure and card content. It handles IDs for boards, cards, lists, labels, user identifiers (`get_me`), and list names.

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