How to Use the Wrike MCP in CrewAI

Q: What specific types of Wrike data does this MCP Server touch?

This server touches user profiles (getuserprofile), task discussion content (comments and attachments), and project hierarchy details.

Run autonomous, specialized teams to manage Wrike projects using CrewAI.

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Connect Wrike MCP to CrewAI

Create your Vinkius account to connect Wrike 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 Wrike with CrewAI

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Perplexity

Autonomous task creation and verification.

Set up Agent A to research the project scope by calling `list_folders_and_projects`. Then, assign Agent B (the 'Action' agent) to use that data to call `create_task` with specific details. The crew runs this sequentially: one agent researches, another acts. You build autonomous operations without manual intervention.

Automated project status auditing.

A 'Monitor' Agent can repeatedly run `list_tasks`, filtering for specific statuses like 'Deferred'. A second 'Moderator' Agent then receives this list and decides if an alert is necessary. The shared memory allows the agents to pass context—the full task info retrieved by one agent becomes input data for another.

Team member reporting and review.

One agent calls `list_team_members` to pull the current directory roster. A second agent then uses that list to cross-reference who has access to a specific project folder. This role specialization allows you to build complex reports where different agents handle data fetching, analysis, and final output generation.

Setup guide

Set up Wrike 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 Wrike tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

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

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

You pass the MCP endpoint URL directly in your Agent mcps list. The agents then use the available tools, like `create_task`, to perform actions autonomously based on their assigned roles.

Yes. You can define specialized roles—one agent researches using `list_tasks` while another analyzes the results. This multi-agent approach is ideal for deep, collaborative analysis.

For simple setup, pass the URL directly. For advanced use, you can utilize `MCPServerHTTP` and even define a `tool_filter` to restrict tool exposure.

It's built for it. The multi-agent framework allows you to build entire pipelines—monitoring, response, escalation—that run without needing a human in the loop at every step.

This server touches user profiles (`get_user_profile`), task discussion content (comments and attachments), and project hierarchy details.

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