How to Use the Height (Project Management) MCP in CrewAI

Q: How do I add Height (Project Management) tools to a CrewAI agent?

Pass the server URL directly into your agent's mcps parameter during initialization. CrewAI will automatically connect to the MCP Server and expose tools like listtasks to that specific agent.

Q: Can a CrewAI agent write or update tickets in Height (Project Management)?

No, this server only supports read operations like gettask and listactivities. Your crew can analyze and report on your project data, but they cannot modify any tickets or lists.

Equip your CrewAI agent teams with Height (Project Management) tools to automate sprint auditing and workload analysis.

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Connect Height (Project Management) MCP to CrewAI

Create your Vinkius account to connect Height (Project Management) 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 Height (Project Management) with CrewAI

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Perplexity

Deploy specialized project audit crews

Your researcher agent pulls active tickets using `list_tasks` while a second analyst agent digs into blocked items. Let your agents collaborate on project tracking instead of doing it yourself by assigning specific roles to your crew. This multi-agent setup uses shared memory to keep context consistent. The researcher agent gathers the raw task data from this MCP Server, and the coordinator agent uses `get_task` to format the status update for your team.

Track workspace progress autonomously

Your coordinator agent calls `list_activities` to see what has changed across the workspace and delegates updates to specialized agents. Keep your crew updated on team momentum without manual input or constant status meetings. By querying the `workspace` tool, your agents understand the overall structure of your boards. They can map recent activity to specific lists and identify which projects are stalling without human intervention.

Analyze team allocation using CrewAI

Your crew checks team bandwidth by pulling the user directory via `list_users` while another agent fetches all active project lists. Set up a dedicated crew to check team bandwidth and identify bottlenecked developers. Because this MCP toolset is read-only, you can let your agents run completely autonomously using `list_lists`. They can analyze workloads and flag potential burnout risks without any danger of modifying or messing up your production boards.

Setup guide

Set up Height (Project Management) 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 Height (Project Management) tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

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

    task = Task(
        description="List recent Height (Project Management) 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 Height. 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 Height (Project Management) MCP in CrewAI

Pass the server URL directly into your agent's `mcps` parameter during initialization. CrewAI will automatically connect to the MCP Server and expose tools like `list_tasks` to that specific agent.

No, this server only supports read operations like `get_task` and `list_activities`. Your crew can analyze and report on your project data, but they cannot modify any tickets or lists.

CrewAI agents use shared memory to pass tool outputs. When one agent calls `list_lists`, the resulting project structures are shared with other agents in the crew to help them coordinate their analysis.

Yes, the server runs on Vinkius's high-performance sandboxes, allowing multiple agents to query tools like `list_users` or `workspace` simultaneously without bottlenecking your execution pipeline.

All retrieved lists, user profiles, and task descriptions are processed in transient, isolated MCP sandboxes. Credentials are encrypted at rest, and no project metadata or task content is logged or stored after the execution ends.

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