How to Use the Asana MCP in CrewAI

Q: How do I connect Asana to CrewAI?

Pass the Vinkius URL directly into the mcps array when defining your agent. The framework automatically parses the available endpoints and loads them.

Q: Can CrewAI filter which Asana tools an agent sees?

Limiting access prevents mistakes. Use the toolfilter parameter on MCPServerHTTP to ensure a junior agent only gets read-only commands.

Deploy autonomous CrewAI teams to monitor, update, and organize your Asana workspaces without human intervention.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect Asana MCP to CrewAI

Create your Vinkius account to connect Asana 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.

GDPR Free for Subscribers

Setup Asana with CrewAI

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Specialized agents for project triage

Assigning `search_tasks` to a dedicated researcher agent lets it hunt down stale tickets. It builds a list of neglected work and passes the context to the next team member. The moderator agent then takes over. Armed with `update_task`, it reassigns those dead tickets to active developers based on the rules you defined.

Shared memory for this MCP Server

Extracting user data via `get_me` feeds directly into your crew's shared context. Every agent knows exactly who owns the current session without asking twice. Mapping out the environment happens once. A scout agent runs `list_workspaces` and `list_projects`, storing the IDs so the rest of the team can navigate the hierarchy blindly.

CrewAI hierarchies for complex planning

Breaking down massive epics requires coordinated effort. Your manager agent evaluates the goal and delegates `create_task` commands to subordinate workers. Tracking activity feeds is just as easy. A monitor agent continuously polls `list_stories` to watch for critical comments and escalate them to human managers.

Setup guide

Set up Asana 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 Asana tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

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

    task = Task(
        description="List recent Asana 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 Asana. 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.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

Connect Asana now

Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about Asana MCP in CrewAI

Pass the Vinkius URL directly into the `mcps` array when defining your agent. The framework automatically parses the available endpoints and loads them.

Limiting access prevents mistakes. Use the `tool_filter` parameter on `MCPServerHTTP` to ensure a junior agent only gets read-only commands.

The sequential execution model prevents bad data. By forcing the agent to run `list_tasks` first, it pulls real IDs before attempting any modifications.

You can use stdio, SSE, or Streamable HTTP. We recommend the HTTP MCP transport for the most stable connection during long-running multi-agent sessions.

Your task descriptions and workspace layouts remain strictly confidential. Our zero-trust architecture guarantees no data lingers after the connection closes.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript