How to Use the Bonusly MCP in CrewAI

Q: How do I register the Bonusly MCP Server with CrewAI?

Pass the server URL directly in your Agent's mcps list. CrewAI automatically discovers tools like givebonus and makes them available to that specific agent.

Q: Can a CrewAI agent give points on Bonusly autonomously?

Yes, you can assign givebonus to a specialized reward agent. The agent uses its role-based logic to decide when and how many points to distribute.

Q: Can I limit which tools my CrewAI culture agent can access?

Yes, use MCPServerHTTP with a toolfilter to restrict access. You can let an analyst agent read data via listbonuses while blocking it from giving points.

Deploy autonomous agent crews to manage Bonusly recognitions and culture metrics with CrewAI.

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Connect Bonusly MCP to CrewAI

Create your Vinkius account to connect Bonusly 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 Bonusly with CrewAI

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Audit your company culture using this CrewAI MCP Server

Let a specialized research agent analyze your team's recognition patterns over time. By passing `list_bonuses` to a CrewAI analyst agent, it can dissect which departments are collaborating most effectively. A separate moderator agent can then take those insights and check them against `list_company_values`. This multi-agent setup helps you spot which corporate pillars are being ignored in daily shoutouts.

Autonomous Peer Recognition Managers

You can build a dedicated escalation crew to handle employee milestones and peer praise with this MCP Server. A monitoring agent tracks team achievements and tasks a sender agent with executing `give_bonus` when goals are met. The crew uses shared memory to ensure they don't double-spend points on the same milestone. They verify the recipient's identity with `get_user` before dispatching any rewards.

Automated Reward and Redemption Tracking

Keep your finance and HR teams aligned by letting agents track budget burn rates autonomously. Your crew can routinely run `list_redemptions` to see what prizes employees are actually claiming. If redemption rates spike, the agent flags the trend and pulls the current standings via `get_leaderboard`. This gives you a clear picture of engagement levels without manual spreadsheet work.

Setup guide

Set up Bonusly 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 Bonusly tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

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

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

Pass the server URL directly in your Agent's `mcps` list. CrewAI automatically discovers tools like `give_bonus` and makes them available to that specific agent.

Yes, you can assign `give_bonus` to a specialized reward agent. The agent uses its role-based logic to decide when and how many points to distribute.

CrewAI's shared memory prevents agents from repeating actions. Once one agent calls `give_bonus`, the rest of the crew sees the completed task and moves on.

Yes, use `MCPServerHTTP` with a `tool_filter` to restrict access. You can let an analyst agent read data via `list_bonuses` while blocking it from giving points.

Yes, all communication goes through Vinkius's zero-trust gateway. Your employee profiles and bonus transaction history are encrypted in transit and never cached by the AI model.

Use it with your favorite AI tools

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