How to Use the AEGIS Hedging MCP in Mastra AI

Build resilient energy trading workflows in Mastra AI. Connect the AEGIS Hedging MCP Server to automate risk management with built-in retries.

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Connect AEGIS Hedging MCP to Mastra AI

Create your Vinkius account to connect AEGIS Hedging 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 AEGIS Hedging with Mastra AI

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Automate Valuation Workflows in Mastra AI

Stop writing manual cron jobs to pull end-of-day numbers. Mastra gives you a workflow engine that handles failures automatically. If the AEGIS API hiccups, your agent just backs off and tries again. Hook up the `get_valuations` tool to your agent. You build a conditional branch: if the mark-to-market drops below a threshold, the workflow alerts a human operator. Otherwise, it logs the data and goes to sleep.

Fetch Forward Curves with Retries

Market data APIs are notoriously flaky during peak trading hours. You need an agent that knows how to wait out network congestion rather than crashing the whole pipeline. When your MCP workflow calls `get_forward_curves`, Mastra applies exponential backoff if the connection drops. The system ensures you actually get your energy forward curves before moving to the next node in the graph.

Audit Hedge Trades Automatically

Compliance teams waste hours digging through trade logs. You replace that manual work with a scheduled Mastra agent that runs autonomously every morning. The agent runs `list_trades` to pull your energy positions. It checks the API status with `check_api_version` first, grabs the trades, and formats a daily risk report. You deploy the whole thing to your cloud provider with a single command.

Setup guide

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

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

const result = await agent.generate(
  "List recent AEGIS Hedging 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 AEGIS Hedging. 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 AEGIS Hedging MCP in Mastra AI

Initialize an MCPClient with your Vinkius URL. Call mcpClient.listTools() and spread the result into your Mastra Agent's tool array. The framework automatically detects the Streamable HTTP transport.

Yes. Enable requireToolApproval on your agent setup. When the system tries to access sensitive trade data, execution pauses until an admin clicks approve.

That is exactly what the framework does best. You pull data using get_valuations and route the workflow down different paths depending on the numbers returned.

The framework catches the error and triggers its built-in retry logic. You configure the backoff intervals to match your specific SLA requirements.

Every request executes inside a zero-trust, single-use V8 Isolate. Your energy market pricing data passes through an encrypted tunnel and the environment self-destructs immediately after the workflow node completes.

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