How to Use the Viral Loops MCP in CrewAI

Q: How does CrewAI run complex campaigns with Viral Loops and MCP Server?

Agents collaborate. One agent might call getmilestones to check for rewards, while another uses the result to trigger a notification action. The shared memory keeps the context clear.

Q: Can CrewAI manage campaign metrics using Viral Loops and MCP Server?

Yes. Agents pull performance stats by calling getcampaignstats. They can then analyze these numbers to decide on the next operational step, like escalating an underperforming campaign.

Q: How does CrewAI handle participant data flow with Viral Loops and MCP Server?

Agents use dedicated tools for this. getparticipant retrieves user details by email, while listparticipants provides a comprehensive list for analysis or bulk actions.

Q: If a reward changes in Viral Loops and CrewAI, how is the agent notified?

The agent can run getrewards to check for configuration updates. If the rewards change, the shared memory alerts all relevant agents that their operational plan needs revision.

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Connect Viral Loops MCP to CrewAI

Create your Vinkius account to connect Viral Loops 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 Viral Loops with CrewAI

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Perplexity

Automate Campaign Discovery and Setup

The team starts by running `list_campaigns` to gather all available campaigns. The monitor agent then uses `get_campaign` on a specific ID to pull the detailed ruleset for that campaign. This structured data allows agents to build complex, multi-step operational plans without human input.

Run Automated Leaderboard Analysis

For leaderboard tasks, an agent calls `list_participants` to gather all current user records. Another specialized agent then uses `get_campaign_stats` to analyze the performance data against the roster. This ensures that automated reporting is always based on the latest campaign metrics.

Manage User Data and Referrals

When a user signs up, an agent calls `create_participant` to register them. Later, another agent can use `get_referral_url` to generate the unique link needed for follow-up actions. The ability to update records via `update_participant` means agents can correct or enrich data automatically.

Setup guide

Set up Viral Loops 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 Viral Loops tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

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

    task = Task(
        description="List recent Viral Loops 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 Viral Loops. 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 Viral Loops MCP in CrewAI

Agents collaborate. One agent might call `get_milestones` to check for rewards, while another uses the result to trigger a notification action. The shared memory keeps the context clear.

Yes. Agents pull performance stats by calling `get_campaign_stats`. They can then analyze these numbers to decide on the next operational step, like escalating an underperforming campaign.

Agents use dedicated tools for this. `get_participant` retrieves user details by email, while `list_participants` provides a comprehensive list for analysis or bulk actions.

This MCP Server touches participant records, specifically email addresses. You handle these through specific calls like `get_participant`.

The agent can run `get_rewards` to check for configuration updates. If the rewards change, the shared memory alerts all relevant agents that their operational plan needs revision.

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