How to Use the Hasura (Instant GraphQL & REST Engine) MCP in Mastra AI

Q: How do I register the Hasura (Instant GraphQL & REST Engine) tools in Mastra AI?

Install @mastra/mcp. Create a new MCPClient instance with your server URL, call listTools(), and spread them into your agent's tool array.

Q: Can Mastra AI automatically retry failed Hasura (Instant GraphQL & REST Engine) queries?

Yes. When executegraphql returns an error, your workflow can catch the exception and trigger a retry with modified parameters.

Q: Does the Hasura (Instant GraphQL & REST Engine) work with Mastra AI human-in-the-loop approvals?

It does. You can wrap destructive tools like executemetadata with requireToolApproval to ensure an admin reviews the payload before execution.

Q: How do I extract raw SQL using this integration?

Call the pgdump tool within your workflow. The agent executes the dump on the connected Postgres source and returns the raw SQL string.

Q: What data privacy protections exist for GraphQL queries?

The executegraphql tool processes your raw user data, including PII and financial records depending on your schema. We secure this by running the MCP Server inside a zero-trust environment where the connection terminates immediately after the query resolves.

Build resilient database workflows in Mastra AI using the Hasura (Instant GraphQL & REST Engine) MCP Server for automated schema management.

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Setup Hasura (Instant GraphQL & REST Engine) with Mastra AI

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Automate Mastra AI Schema Updates

The `execute_metadata` tool allows your Mastra AI workflows to modify database relationships and permissions programmatically. Your agent can track schema changes, validate them, and apply the metadata updates automatically. If a migration fails, Mastra catches the error and retries the operation using its built-in exponential backoff. You define the fallback logic, and the MCP Server executes the exact JSON payload required to fix the state.

Verify Health Before Queries

The `check_health` tool gives your workflows a mandatory pre-flight check before running heavy database operations. You can configure it to use strict mode, which verifies that all metadata remains consistent with the underlying Postgres schema. When the check passes, the workflow proceeds to data extraction. If it fails, the agent pauses execution and alerts an administrator, preventing corrupt data from entering your pipeline.

Extract Configurations Automatically

The `get_config` and `get_version` tools pull down the exact server settings and Hasura engine type currently running in production. Your agent reads these details to determine which API features are available for a given workflow branch. This prevents version mismatch errors during automated deployments. The workflow adapts its behavior based on the live configuration, ensuring smooth execution across different environments.

Setup guide

Set up Hasura (Instant GraphQL & REST Engine) 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 Hasura (Instant GraphQL & REST Engine) 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: "hasura-instant-graphql-rest-engine-mcp-client",
  servers: {
    "hasura-instant-graphql-rest-engine-mcp": {
      url: new URL(
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
      ),
    },
  },
});

const agent = new Agent({
  name: "Hasura (Instant GraphQL & REST Engine) Agent",
  model: openai("gpt-4o"),
  instructions: "You have access to Hasura (Instant GraphQL & REST Engine) tools.",
  tools: {
    ...(await mcpClient.listTools()),
  },
});

const result = await agent.generate(
  "List recent Hasura (Instant GraphQL & REST Engine) transactions"
);
console.log(result.text);

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Common questions about Hasura (Instant GraphQL & REST Engine) MCP in Mastra AI

Install `@mastra/mcp`. Create a new `MCPClient` instance with your server URL, call `listTools()`, and spread them into your agent's tool array.

Yes. When `execute_graphql` returns an error, your workflow can catch the exception and trigger a retry with modified parameters.

It does. You can wrap destructive tools like `execute_metadata` with `requireToolApproval` to ensure an admin reviews the payload before execution.

Call the `pg_dump` tool within your workflow. The agent executes the dump on the connected Postgres source and returns the raw SQL string.

The `execute_graphql` tool processes your raw user data, including PII and financial records depending on your schema. We secure this by running the MCP Server inside a zero-trust environment where the connection terminates immediately after the query resolves.

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