How to Use the Cincopa MCP in CrewAI
Deploy an autonomous crew of agents to manage your Cincopa media library with CrewAI.
Works with every AI agent you already use
…and any MCP-compatible client
Connect Cincopa MCP to CrewAI
Create your Vinkius account to connect Cincopa 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.
Assemble a Specialist Media Team
With CrewAI, you don't just get one agent; you build a team. Create a 'Librarian' agent that uses `list_cincopa_assets` and `get_asset_metadata` to catalog your library. It can pass a summary to a 'Publisher' agent, all communicating via one Cincopa MCP Server. The Publisher agent's job is to act. It can take the list of new assets, find a template with `list_cincopa_templates`, and then create a new gallery using `create_new_gallery`. Each agent has a clear role.
Automate Content Moderation and Cleanup
Set up a 'Moderator' agent that periodically scans assets. It could use `get_asset_metadata` to check for missing descriptions or tags. When it finds an issue, it can delegate the task to a 'Cleanup' agent. This Cleanup agent has one job: fix or remove assets. It could be given permission to use `delete_media_asset` on items that are old or flagged by the Moderator. This divides the labor, letting you build a more complex and reliable operation.
Run Sequential Publishing Workflows
CrewAI supports sequential tasks, which is perfect for publishing. The first agent in the sequence can be tasked with getting an upload link via `get_media_upload_url` and uploading a new video. Once that task is complete, it hands off to the next agent. The second agent's job is to verify the upload with `get_asset_metadata` and then use `get_gallery_details` to add it to the correct gallery. It's an assembly line for your content.
Set up Cincopa 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 Cincopa tools as needed.
from crewai import Agent, Task, Crew
agent = Agent(
role="Cincopa Analyst",
goal="Access and analyze Cincopa data via MCP.",
backstory="Expert analyst with direct Cincopa access.",
mcps=[
"https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
],
)
task = Task(
description="List recent Cincopa 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="Cincopa Analyst",
goal="Access and analyze Cincopa data via MCP.",
backstory="Expert analyst with direct Cincopa access.",
tools=mcp_tools,
)
task = Task(
description="List recent Cincopa 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 Cincopa. 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 Cincopa MCP in CrewAI
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