How to Use the SkootEco MCP in Mastra AI

Q: What tools are available in the SkootEco MCP Server for mastra-ai?

The server exposes 18 tools. Key ones include getemissions, listprojects, and purchaseoffset. You can select which specific tools your workflow needs to access.

Automated Compliance Workflows: Build Multi-Step SkootEco Processes with mastra-ai.

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Connect SkootEco MCP to Mastra AI

Create your Vinkius account to connect SkootEco to Mastra AI — we handle the hosting, security, and runtime updates so you don't have to. No server setup required.

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Setup SkootEco with Mastra AI

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ChatGPT

Claude

Perplexity Vinkius runs on Gemini

Gemini

Grok

Complex MCP Server Auditing for mastra-ai

Set up a full audit workflow that runs autonomously. The agent first calls `check_skooteco_status`. If the connection is good, it chains together calls to `get_emissions` and `list_categories` to build an emissions baseline. If any step fails or times out, the workflow doesn't just stop; it retries with backoff. This reliable chain ensures that your compliance checks are exhaustive. You get a guaranteed sequence of actions: check status -> gather raw data -> generate summary reports.

Automated Offset Strategy using mastra-ai

The agent can build an intelligent mitigation plan. It first runs `get_impact_profile` to determine the required offset amount. Then, it executes a sequence of actions: checking available projects (`list_projects`), selecting the best option, and finally calling `purchase_offset`. If the purchase fails due to rate limits, the agent automatically retries with a different project. This handles real-world API failures gracefully. You're deploying complex business logic, not just simple tool calls.

Conditional Compliance Reporting via MCP Server

Build workflows that depend on data availability. For example, the agent runs `get_report`. If the report indicates Scope 3 emissions are above a certain threshold, the workflow automatically triggers a call to `add_emission` for manual review and then calls `get_account` to check spending limits before proceeding. This level of conditional branching means you're building decision engines. The system decides what tool runs next based on the output of the previous one.

Setup guide

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

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

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

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Common questions about SkootEco MCP in Mastra AI

It handles failures using exponential backoff and defined retry logic. If a tool call fails, it doesn't crash; it tries again with increasing wait times until the process completes or hits a hard limit.

Yes. You can chain together dozens of tool calls, such as getting emissions by scope, listing projects, and then purchasing offsets—all in one reliable workflow.

The server handles emission records (Scope 1, 2, 3), account information, project details, and offset purchases. It's a wide range of financial and environmental metrics.

The agent framework is built for reliability. Its ability to handle state and automatically retry operations makes it ideal for crucial, non-negotiable tasks like compliance audits.

The server exposes 18 tools. Key ones include `get_emissions`, `list_projects`, and `purchase_offset`. You can select which specific tools your workflow needs to access.

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