How to Use the Calendarific MCP in CrewAI
Coordinate multi-agent teams with shared global holiday awareness in CrewAI.
Works with every AI agent you already use
…and any MCP-compatible client
Connect Calendarific MCP to CrewAI
Create your Vinkius account to connect Calendarific 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.
Equip CrewAI specialized agents with global holiday context
Your CrewAI research agent queries `list_supported_countries` to identify target markets, while a scheduling agent uses `list_holidays` to map out campaign dates. CrewAI coordinates these specialized agents, allowing them to share holiday data through a unified memory system. To set this up, pass your Vinkius HTTP endpoint for Calendarific directly into the CrewAI agent configuration using the `mcps` list. The crew automatically shares access to all holiday tools without requiring manual wiring for each agent.
Filter holiday data across sequential crew tasks
A CrewAI moderator agent filters incoming holiday data using `list_holidays_by_type` before passing the list to an execution agent. This sequential pipeline ensures your autonomous crew only processes relevant national or religious events. You don't have to manually pass keys; use the CrewAI MCP library to restrict agent access to sensitive tools like `get_account_info`. This keeps junior agents focused entirely on public holiday lookups.
Coordinate localized operations using regional MCP Server tools
Your autonomous CrewAI crew manages localized operational schedules by calling `list_holidays_by_location` across different team agents. One agent monitors US state holidays while another tracks German regional holidays, coordinating handoffs in real-time. The CrewAI agents use shared memory to translate language codes via `list_supported_languages` and localize holiday names automatically. Because CrewAI supports standard HTTP and SSE transports, your agents run queries against the Calendarific endpoint without latency.
Set up Calendarific 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 Calendarific tools as needed.
from crewai import Agent, Task, Crew
agent = Agent(
role="Calendarific Analyst",
goal="Access and analyze Calendarific data via MCP.",
backstory="Expert analyst with direct Calendarific access.",
mcps=[
"https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
],
)
task = Task(
description="List recent Calendarific 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="Calendarific Analyst",
goal="Access and analyze Calendarific data via MCP.",
backstory="Expert analyst with direct Calendarific access.",
tools=mcp_tools,
)
task = Task(
description="List recent Calendarific 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 Calendarific. 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 Calendarific MCP in CrewAI
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