How to Use the Goldsky (Web3 Data Indexing & Subgraphs) MCP in Mastra AI

Build autonomous Web3 infrastructure workflows with Mastra AI and Goldsky (Web3 Data Indexing & Subgraphs).

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Connect Goldsky (Web3 Data Indexing & Subgraphs) MCP to Mastra AI

Create your Vinkius account to connect Goldsky (Web3 Data Indexing & Subgraphs) 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 Goldsky (Web3 Data Indexing & Subgraphs) with Mastra AI

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Automated Pipeline Recovery in Mastra AI

Configure your agents to watch for failures. Use `get_pipeline_error_count` to trigger a `restart_pipeline` command automatically when thresholds are exceeded. Your workflow handles the recovery logic for you. It keeps your indexing running without manual intervention.

Deploy Indexing Pipelines via Mastra AI

Define your infrastructure as code using the agent framework. Call `create_pipeline` to spin up new subgraphs based on your custom logic. It turns your deployment into a repeatable, automated step. You just pass the config and let the agent handle the rest.

Audit Indexing Health with Mastra AI

Set up a recurring agent task to check `list_pipelines` and report status to your admin dashboard. It gives you a clear view of your infrastructure health. Your agents maintain the inventory. You get notified only if something goes wrong.

Setup guide

Set up Goldsky (Web3 Data Indexing & Subgraphs) 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 Goldsky (Web3 Data Indexing & Subgraphs) 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: "goldsky-web3-data-indexing-subgraphs-mcp-client",
  servers: {
    "goldsky-web3-data-indexing-subgraphs-mcp": {
      url: new URL(
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
      ),
    },
  },
});

const agent = new Agent({
  name: "Goldsky (Web3 Data Indexing & Subgraphs) Agent",
  model: openai("gpt-4o"),
  instructions: "You have access to Goldsky (Web3 Data Indexing & Subgraphs) tools.",
  tools: {
    ...(await mcpClient.listTools()),
  },
});

const result = await agent.generate(
  "List recent Goldsky (Web3 Data Indexing & Subgraphs) transactions"
);
console.log(result.text);

Get your connection token →

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Common questions about Goldsky (Web3 Data Indexing & Subgraphs) MCP in Mastra AI

Yes. You add the MCP server URL to your Mastra agent config. It automatically maps the indexing tools to your agent's capabilities.

It can. You define your workflow steps to retry pipeline operations if they fail. It’s built into the framework’s core engine.

You use the built-in tool approval settings. Every sensitive action like deleting a pipeline can require a human to sign off before the agent executes it.

Your agent detects the failure state via the status tool. You can then script a custom branching path to resolve the issue.

Your indexing configurations are isolated by your endpoint token. No other agent or project can touch your specific pipeline definitions.

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