How to Use the LiquidPlanner Classic MCP in CrewAI
Deploy a crew of autonomous CrewAI agents to manage LiquidPlanner Classic schedules, assign tasks, and balance workloads.
Works with every AI agent you already use
…and any MCP-compatible client
Connect LiquidPlanner Classic MCP to CrewAI
Create your Vinkius account to connect LiquidPlanner Classic 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.
Coordinate multi-agent project planning
`create_project` allows your planning agent to establish new initiatives in your default workspace. While that agent sets up the project container, a second agent uses `create_task` to populate the backlog. CrewAI coordinates these actions sequentially, ensuring tasks are only created after the project container exists. This multi-agent collaboration prevents broken dependencies and keeps your workspaces clean.
Automate resource allocation with CrewAI
`list_members` retrieves the active team list, which your resource allocation agent analyzes to find available people. A separate coordinator agent then assigns tasks based on individual capacity. This division of labor keeps your project schedules realistic. The agents communicate through shared memory to ensure no single developer gets double-booked.
Monitor and adjust task dependencies
`list_tasks` feeds the current task list to a monitoring agent that watches for overdue items. When a delay is found, the agent calculates the downstream impact on other tasks. The moderator agent then uses `update_task` to shift dates and resolve conflicts. Your entire scheduling pipeline runs autonomously without requiring constant manual oversight.
Set up LiquidPlanner Classic MCP in CrewAI
Prerequisites
- Python 3.10+ installed
-
crewaipackage (pip install crewai) - Active Vinkius subscription with a valid endpoint token
- 1
Install CrewAI
Run
pip install crewaito install the framework. MCP support is built-in via themcpsparameter. - 2
Add the MCP URL to your agent
Pass your Vinkius endpoint directly to the
mcpslist. 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
Crewwith your agent and tasks. Callcrew.kickoff()— the agent will automatically invoke LiquidPlanner Classic tools as needed.
from crewai import Agent, Task, Crew
agent = Agent(
role="LiquidPlanner Classic Analyst",
goal="Access and analyze LiquidPlanner Classic data via MCP.",
backstory="Expert analyst with direct LiquidPlanner Classic access.",
mcps=[
"https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
],
)
task = Task(
description="List recent LiquidPlanner Classic transactions",
agent=agent,
expected_output="A summary of recent activity",
)
crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result) Prerequisites
- Python 3.10+ installed
-
crewai+crewai-toolspackages - Active Vinkius subscription with a valid endpoint token
- 1
Install dependencies
Run
pip install crewai crewai-tools. TheMCPServerAdapterhandles lifecycle management and tool conversion. - 2
Connect with MCPServerAdapter
Use
MCPServerAdapteras a context manager withSseServerParameterspointing to your Vinkius endpoint. The adapter automatically manages connection lifecycle. - 3
Assign tools and run
Pass the returned
mcp_toolsto your agent'stoolsparameter. The adapter converts MCP tools to nativeBaseToolobjects compatible with all CrewAI agents.
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="LiquidPlanner Classic Analyst",
goal="Access and analyze LiquidPlanner Classic data via MCP.",
backstory="Expert analyst with direct LiquidPlanner Classic access.",
tools=mcp_tools,
)
task = Task(
description="List recent LiquidPlanner Classic transactions",
agent=agent,
expected_output="A summary of recent activity",
)
crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result) Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by LiquidPlanner Classic. 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 LiquidPlanner Classic MCP in CrewAI
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