How to Use the OceanBase MCP in Mastra AI

Q: How do I connect Mastra AI to my OceanBase MCP Server?

Instantiate the client using new MCPClient and point it to your Vinkius server URL. Then, fetch the active tools with mcpClient.listTools() and register them directly inside your agent configuration.

Q: Can Mastra AI automatically retry failed OceanBase queries?

Yes. If tools like getresourcestats fail due to temporary network blips, the framework uses exponential backoff to retry the call. You don't have to write manual try-catch loops for database telemetry.

Q: How do I require human approval before Mastra AI modifies an OceanBase tenant?

Set the requireToolApproval flag on sensitive tools like gettenantdetails. The agent will pause its workflow and wait for an operator's OK before proceeding with any resource changes.

Q: Can I use conditional branching based on OceanBase database lists?

Yes, you can route your workflows based on what listdatabases returns. For example, if a specific schema is missing, you can branch to a step that creates it or alerts the team.

Build self-healing OceanBase workflows with Mastra AI agents that monitor and optimize cluster resources automatically.

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Connect OceanBase MCP to Mastra AI

Create your Vinkius account to connect OceanBase 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 OceanBase with Mastra AI

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Automated Cluster Recovery Workflows

`list_clusters` gives your Mastra AI agents the visibility they need to monitor distributed node health across your global deployments using the MCP Server. When a cluster reports degraded status, your workflow triggers diagnostic steps instantly. By feeding the output of `get_cluster_details` into a conditional branching step, the agent can decide whether to alert an on-call engineer or initiate an automated tenant migration. You define the logic, and the framework executes the recovery.

Auto-Scaling Tenants with Mastra AI Workflows

`get_resource_stats` acts as the primary telemetry source for your agent's automated scaling decisions. If CPU utilization crosses your threshold, the workflow automatically triggers a capacity adjustment. The agent verifies the target resources using `get_tenant_details` before making changes, preventing allocation overshoots. If the action fails, the built-in backoff engine retries the operation without human intervention.

Automated Schema Auditing

`list_databases` lets your workflow scan for orphaned schemas across all registered projects via MCP. The agent runs this check on a cron schedule to keep your storage footprints clean. Integrating `list_projects` ensures the agent maps every database back to its rightful owner. If it finds an unassigned schema, it triggers a Slack notification with the exact resource stats.

Setup guide

Set up OceanBase 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 OceanBase 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: "oceanbase-mcp-client",
  servers: {
    "oceanbase-mcp": {
      url: new URL(
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
      ),
    },
  },
});

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

const result = await agent.generate(
  "List recent OceanBase transactions"
);
console.log(result.text);

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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 OceanBase. 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 OceanBase MCP in Mastra AI

Instantiate the client using `new MCPClient` and point it to your Vinkius server URL. Then, fetch the active tools with `mcpClient.listTools()` and register them directly inside your agent configuration.

Yes. If tools like `get_resource_stats` fail due to temporary network blips, the framework uses exponential backoff to retry the call. You don't have to write manual try-catch loops for database telemetry.

Set the `requireToolApproval` flag on sensitive tools like `get_tenant_details`. The agent will pause its workflow and wait for an operator's OK before proceeding with any resource changes.

Yes, you can route your workflows based on what `list_databases` returns. For example, if a specific schema is missing, you can branch to a step that creates it or alerts the team.

Your database access credentials never touch your agent's execution environment directly. Vinkius isolates the connection details, and only the raw statistics from tools like `list_instances` are passed to your workflow engine.

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