How to Use the BattleMetrics MCP in Mastra AI

Q: Can I build a ban-checking workflow with BattleMetrics and Mastra AI?

Absolutely. You set up a sequence where the agent calls listbans, iterates through the results, and executes a secondary action if a specific player ID appears.

Build complex, automated monitoring workflows for Mastra AI using BattleMetrics data.

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Connect BattleMetrics MCP to Mastra AI

Create your Vinkius account to connect BattleMetrics 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 BattleMetrics with Mastra AI

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Automated server monitoring for Mastra AI

Trigger workflows that watch server health using `get_server`. If player counts drop, your agent can react immediately based on the live metadata returned. Use `search_servers` to scan for specific criteria across the entire database. It allows your Mastra AI agents to find targets for investigation before you even lift a finger.

Agentic player auditing and session history

Automate your background checks by calling `get_player` and `get_player_sessions`. Your agent can flag suspicious activity patterns without manual intervention. Combine these tools to build a pipeline that monitors for specific behavior. Once the agent identifies a player ID, it fetches their entire history to confirm the status.

Manage bans and game ecosystem data

Keep your community rules enforced by using `list_bans` to track enforcement actions. Your agent can cross-reference these records automatically. Use `list_games` to ensure your workflow is targeting the correct game ecosystem. It ensures your agent always requests data from the right source.

Setup guide

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

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

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

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Common questions about BattleMetrics MCP in Mastra AI

Mastra AI includes built-in workflow logic for retries. If a request to `get_server` fails, the engine can automatically retry the call based on your policy.

Absolutely. You set up a sequence where the agent calls `list_bans`, iterates through the results, and executes a secondary action if a specific player ID appears.

The server provides a standard interface that Mastra AI detects automatically. You just point the agent to the URL and it handles the rest.

You get detailed info like server rank, map status, and player counts. The tools return structured JSON that your agents parse to make decisions.

Your connection is encrypted and scoped to the specific tools required for your workflow. The sandbox environment limits exposure to only the session at hand.

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