How to Use the Glassnode (On-chain Data) MCP in Google ADK

Q: How do I configure the Glassnode (On-chain Data) tools inside a Google ADK project?

Install the framework via pip install google-adk and create an McpToolset pointing to your Vinkius HTTP URL. Pass this toolset into your LlmAgent constructor to give your Gemini models instant access to the tools.

Q: Can I restrict my Google Cloud agent to only read-only asset lists?

Yes, you can use the optional toolnames filter in the ADK toolset setup to only expose listassets and listmetrics. This prevents the agent from making unnecessary or expensive metric queries.

Run enterprise-grade crypto market analysis by connecting the Google ADK directly to Glassnode on-chain data.

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Connect Glassnode (On-chain Data) MCP to Google ADK

Create your Vinkius account to connect Glassnode (On-chain Data) 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 Glassnode (On-chain Data) with Google ADK

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Long-context metric analysis via Google ADK

The `list_metrics` tool returns the complete index of available Glassnode data points directly to your Gemini agent. Because Gemini supports million-token context windows, your agent can process this entire list of metric paths to select the best indicators for a specific token. You initialize the toolset using `McpToolset` with your Vinkius HTTP endpoint. The Google ADK maps these tools into your agent's execution loop, allowing it to cross-reference on-chain indicators with your existing BigQuery datasets.

Deep asset discovery for enterprise data pipelines

The `list_assets` tool pulls the current list of 1000+ supported tokens directly into your Gemini-powered workspace. Your agent uses this tool to map out which assets have active on-chain metrics, preparing your pipeline for targeted data retrieval. This integration lets you build automated market scanners that run on Google Cloud. The ADK handles transport negotiation over HTTP or Stdio, ensuring your agent gets clean JSON payloads it can write directly to cloud storage.

Detailed metric exploration with this MCP Server

The `get_metric_details` tool provides descriptions and parameter requirements for any on-chain metric on the platform. Your agent calls this tool to understand the math behind indicators like NUPL or MVRV before executing a trade decision. Deploying this MCP Server inside your Google ADK workflow gives Gemini the exact technical context it needs to write accurate analysis reports. You get precise, data-backed summaries instead of generic market commentary.

Setup guide

Set up Glassnode (On-chain Data) 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 Glassnode (On-chain Data) 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="Glassnode (On-chain Data)_agent",
    model="gemini-2.0-flash",
    instruction="You have access to Glassnode (On-chain Data) 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 Glassnode. 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 Glassnode (On-chain Data) MCP in Google ADK

Install the framework via `pip install google-adk` and create an `McpToolset` pointing to your Vinkius HTTP URL. Pass this toolset into your `LlmAgent` constructor to give your Gemini models instant access to the tools.

Yes, Gemini's massive context window is perfect for handling the large JSON arrays returned by `get_bulk_metric`. The model can ingest raw on-chain metrics for hundreds of assets simultaneously without running out of memory.

Yes, Vinkius handles the authorization layer using a single endpoint token. Your Google ADK agent simply talks to the Vinkius proxy, which securely signs requests to the Glassnode API without exposing credentials to your application code.

Yes, you can use the optional `tool_names` filter in the ADK toolset setup to only expose `list_assets` and `list_metrics`. This prevents the agent from making unnecessary or expensive metric queries.

Your asset queries and metric request histories pass through ephemeral Vinkius V8 isolates that destroy execution state immediately after completion. No on-chain query parameters or target asset lists are stored on disk, keeping your proprietary market research completely private.

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