How to Use the Galxe MCP in Google ADK

Connect Gemini agents to Galxe using Google ADK for massive Web3 campaigns.

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Connect Galxe MCP to Google ADK

Create your Vinkius account to connect Galxe to Google ADK 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 Galxe with Google ADK

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Query Web3 Quests with Gemini

Running a massive Web3 campaign generates a ton of fragmented data. By plugging this MCP Server into Google ADK, your Gemini agents can pull live Galxe campaign details and cross-reference them with your BigQuery datasets. The agent calls `get_quest` to pull down the exact parameters of a live campaign. Because Gemini handles massive context windows, you can feed it fifty different quest configurations at once and ask it to find overlapping requirements or structural flaws.

Cross-Reference Leaderboard Data

Leaderboards are only useful if you actually do something with the data. Your Google Cloud-based agents can now read Galxe rankings and immediately act on them using your existing enterprise infrastructure. Using `get_space_leaderboard` and `get_starboard_leaderboard`, the agent grabs the current top users in your ecosystem. It can then pipe that data into Vertex AI to predict which users are likely to churn, or write the results directly to a BigQuery table for your analytics team.

Automate Credential Updates

Manual allowlist management doesn't scale when you have thousands of participants. Let your Gemini agent handle the tedious work of updating user eligibility based on complex on-chain or off-chain triggers. When your backend detects a valid user action, the agent triggers `mutate_credential_items` to update the Galxe API credential. You can use the tool names filter in Google ADK to ensure only specific, highly trusted agents have access to this mutation tool.

Setup guide

Set up Galxe MCP in Google ADK

Prerequisites

Python 3.10+ installed
google-adk package (pip install google-adk)
Active Vinkius subscription with a valid endpoint token

1

Install Google ADK

Run pip install google-adk to install the Agent Development Kit. MCP support is included via the McpToolset class.
2

Connect via SSE transport

Use McpToolset.from_server() with SseServerParams pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create an LlmAgent

Pass the returned mcp_tools list directly to LlmAgent(tools=mcp_tools). The ADK maps each MCP tool to a native Gemini function call — no manual schema definitions required.
4

Run with any Gemini model

The agent works with any Gemini model (gemini-2.0-flash, gemini-2.5-pro, etc.). Copy the full example on the right to get started with Galxe tools in your ADK agent.

agent.py

from google.adk.agents import LlmAgent
from google.adk.tools.mcp_tool.mcp_toolset import McpToolset
from google.adk.tools.mcp_tool.mcp_session_manager import SseServerParams

# Connect to the MCP via SSE
mcp_tools, exit_stack = await McpToolset.from_server(
    connection_params=SseServerParams(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    )
)

# Create your agent with auto-discovered tools
agent = LlmAgent(
    name="Galxe_agent",
    model="gemini-2.0-flash",
    instruction="You have access to Galxe tools via MCP.",
    tools=mcp_tools,
)

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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.

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Common questions about Galxe MCP in Google ADK

Set up a McpToolset using StreamableHttpServerParameters with your connection URL. Then pass it to your LlmAgent via the tools array.

Yes. You can apply a tool names filter during setup. This lets you expose read-only operations like check_quest_eligibility while hiding mutation tools from public-facing agents.

It reads the JSON output from the tools directly into its context window. You can pull massive leaderboards and rely on Gemini's 1M+ token limit to analyze the entire dataset at once.

The integration supports both Stdio and HTTP transports. For managed cloud deployments, HTTP is usually the most straightforward path.

The MCP Server processes Web3 wallet addresses and Twitter handles for leaderboard rankings. When your agent reads this data into Google Cloud, it falls under your GCP security perimeter and IAM policies.

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