How to Use the Glassnode (On-chain Data) MCP in Mastra AI

Build resilient trading workflows with Mastra AI and the Glassnode (On-chain Data) MCP Server.

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Connect Glassnode (On-chain Data) MCP to Mastra AI

Create your Vinkius account to connect Glassnode (On-chain Data) to Mastra AI 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 Mastra AI

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Automated workflows with Mastra AI

Create complex logic flows that trigger based on on-chain events. Use `get_metric` to feed your Mastra agents, allowing them to make decisions without human input. If the data indicates a shift, the agent executes the next step in your sequence automatically. It handles the logic while you define the strategy.

Point-in-time analysis for Mastra AI

Backtest your agent strategies using `get_pit_metric`. This allows you to verify how your Mastra workflow would have performed during past market volatility. Eliminate look-ahead bias by providing the agent with the exact data that was available at that specific moment in history.

Metadata management in Mastra AI

Identify which metrics to track using `list_metrics` and `get_metric_details`. This helps your Mastra agents understand the parameters before they query the data. Your agent checks the metric definition first, ensuring it requests the correct data format. This avoids errors in your automated pipeline.

Setup guide

Set up Glassnode (On-chain Data) MCP in Mastra AI

Prerequisites

Node.js 18+ and a TypeScript project
@mastra/mcp + @mastra/core packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run npm install @mastra/mcp @mastra/core plus your preferred model provider (e.g. @ai-sdk/openai).
2

Configure the MCPClient
Create an MCPClient with your Vinkius endpoint as a URL object. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Discover and inject tools
Call mcpClient.listTools() and spread the result into your agent's tools object. All Glassnode (On-chain Data) tools become native Mastra tools.
4

Run with any model
Swap openai("gpt-4o") for any AI SDK-compatible provider. Call agent.generate() and the agent routes tool calls through MCP automatically.

agent.ts

import { MCPClient } from "@mastra/mcp";
import { Agent } from "@mastra/core/agent";
import { openai } from "@ai-sdk/openai";

const mcpClient = new MCPClient({
  id: "glassnode-on-chain-data-mcp-client",
  servers: {
    "glassnode-on-chain-data-mcp": {
      url: new URL(
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
      ),
    },
  },
});

const agent = new Agent({
  name: "Glassnode (On-chain Data) Agent",
  model: openai("gpt-4o"),
  instructions: "You have access to Glassnode (On-chain Data) tools.",
  tools: {
    ...(await mcpClient.listTools()),
  },
});

const result = await agent.generate(
  "List recent Glassnode (On-chain Data) transactions"
);
console.log(result.text);

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Common questions about Glassnode (On-chain Data) MCP in Mastra AI

Add the server URL to your agent configuration. The framework will auto-detect the tools, which you then spread into your agent toolset.

Yes. Mastra supports automatic retries with exponential backoff, which is ideal for handling intermittent network issues when calling the API.

Yes. You can enable tool approval, which forces the agent to ask for your permission before it executes a sensitive data query.

It provides institutional-grade data. When paired with the framework's retry logic, it forms a solid base for your automated operations.

Your API key is injected via secure environment variables. All communication between the agent and the server is handled through a private, authenticated transport.

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