How to Use the Zealy MCP in AutoGen

Q: How do I list available Zealy content with AutoGen?

You can run a simulation where agents compare listquests and listsecrets. The result is a negotiated plan that details which assets should be prioritized for the next community cycle.

Run complex decisions and debates on Zealy using AutoGen.

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Connect Zealy MCP to AutoGen

Create your Vinkius account to connect Zealy to AutoGen 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 Zealy with AutoGen

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Moderating Disputes and Decisions

When a submission needs approval, multiple agents can debate the merits of `review_submission`. One agent might argue for rejection based on rules, while another recommends acceptance. The final consensus guides your action. Security agents can proactively flag risks by monitoring user data fetched with `get_user` before an action is taken. This built-in deliberation process ensures no single point fails.

Designing and Launching Campaigns

Building a new campaign requires planning. One agent can research the current community state using `get_community`, while another drafts the goal using `create_quest`. They negotiate until the perfect quest structure is defined. If you need to adjust goals, agents can compare existing quests (`list_quests`) against new ideas, ultimately deciding on the best parameters for an update via `update_quest`.

Managing System Integration and Flow

When linking Zealy to external systems, multiple agents collaborate. One manages the technical setup using `create_webhook`, while another tests it by simulating a data flow through `list_webhooks`. They verify the endpoint works before completion. For complex user tracking, agents can compare results from `get_leaderboard` against manual XP adjustments (`add_xp`), ensuring the recorded state matches the intended outcome.

Setup guide

Set up Zealy MCP in AutoGen

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] package
Active Vinkius subscription with a valid endpoint token

1

Install AutoGen with MCP
Run pip install "autogen-ext[mcp]" autogen-agentchat. The MCP extension includes mcp_server_tools for stateless tool access.
2

Fetch tools from the MCP
Call mcp_server_tools(SseServerParams(url=...)) with your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Run your agent
Pass the tools to AssistantAgent and call agent.run(). The agent invokes Zealy tools and returns structured results.

agent.py

from autogen_ext.tools.mcp import SseServerParams, mcp_server_tools
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

tools = await mcp_server_tools(server_params)

agent = AssistantAgent(
    name="Zealy_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent Zealy data")
print(result.messages[-1].content)

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Prerequisites

Python 3.10+ installed
autogen-ext[mcp] + autogen-agentchat
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Same packages as above. McpWorkbench is ideal when your agent needs stateful sessions across multiple tool calls.
2

Use McpWorkbench as context manager
Wrap your agent in async with McpWorkbench(...) to maintain shared state and resources. The workbench manages the full MCP session lifecycle.
3

Run with workbench
Pass workbench=workbench to your agent. State is preserved across multiple tool calls within the same session.

agent.py

from autogen_ext.tools.mcp import McpWorkbench, SseServerParams
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

async with McpWorkbench(server_params) as workbench:
    agent = AssistantAgent(
        name="Zealy_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent Zealy data")
    print(result.messages[-1].content)

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 Zealy. 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 Zealy MCP in AutoGen

Agents can debate the best way to interpret ranking data. They use `get_leaderboard` results, comparing all-time performance against recent sprints to give you a nuanced report that goes beyond simple numbers.

Yes. If an agent needs to verify if someone is in the community, it uses `check_member`. The multi-agent setup ensures that this verification step is challenged by a second agent for accuracy before proceeding.

The system supports complex moderation workflows. Agents can negotiate whether to `ban_user` or if a simple warning (via manual XP adjustment) is sufficient, providing multiple viewpoints on the best course of action.

You can run a simulation where agents compare `list_quests` and `list_secrets`. The result is a negotiated plan that details which assets should be prioritized for the next community cycle.

The server handles user profiles, including fetching user details by Zealy ID. Because of its multi-agent structure, you can build consensus rules that automatically check for PII compliance before any tool is called.

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