How to Use the Galxe MCP in AutoGen

Q: How do AutoGen agents resolve conflicts when checking Galxe eligibility?

If one agent gets a false result from checkcredentialeligibility but another sees high loyalty points via getspaceleaderboard, they debate the discrepancy in the chat log until they reach a programmed consensus.

Let AutoGen agents debate and coordinate Web3 community operations using the Galxe MCP Server.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect Galxe MCP to AutoGen

Create your Vinkius account to connect Galxe 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.

GDPR Free for Subscribers

Setup Galxe with AutoGen

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Multi-agent AutoGen consensus for Galxe campaigns

Set up a multi-agent debate in AutoGen to manage your community whitelist. A verification agent can run `check_credential_eligibility` while a security agent reviews the wallet history before making updates. Once both agents agree the user is legitimate, a third agent executes `mutate_credential_items`. This prevents single-agent errors and secures your Web3 credentials behind a consensus-driven workflow.

Coordinate Twitter and on-chain Galxe leaderboards

Use multiple AutoGen agents to analyze community engagement across platforms. One agent pulls Twitter influence metrics with `get_starboard_leaderboard` while another tracks loyalty points via `get_space_leaderboard`. The coordinator agent then negotiates how to distribute custom rewards based on both datasets. This multi-perspective analysis ensures your most active cross-platform supporters get recognized.

Automated quest eligibility troubleshooting

Let your AutoGen support agents work together to solve user issues. When a user complains, one agent retrieves the quest details using `get_quest` while another checks the user's status with `check_quest_eligibility`. They compare notes in the conversation thread to diagnose the exact blocker. The final response is delivered to the user with a clear, verified explanation of what went wrong.

Setup guide

Set up Galxe 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 Galxe 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="Galxe_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

Get your connection token →

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="Galxe_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent Galxe 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 Galxe. 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.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

Connect Galxe now

Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about Galxe MCP in AutoGen

You use the AutoGen MCP extension to load the tools and pass them to your `AssistantAgent`. The adapter handles translating the schemas so agents can call `get_quest` during conversations.

You can configure the agent's human-in-the-loop setting. For sensitive writes like `mutate_credential_items`, you can require a human admin to approve the action in the terminal before execution.

If one agent gets a false result from `check_credential_eligibility` but another sees high loyalty points via `get_space_leaderboard`, they debate the discrepancy in the chat log until they reach a programmed consensus.

Yes. You can connect your agents to the MCP Server using the streamable HTTP parameters, enabling real-time streaming of quest data directly into agent conversations.

It only queries public on-chain credentials and Twitter metrics needed for `check_quest_eligibility`. Data transmission is protected by TLS encryption, and Vinkius's zero-trust runtime ensures no session data is cached on disk.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript