How to Use the ChartHop MCP in Mastra AI

Q: Can a Mastra AI agent wait for approval before querying ChartHop?

Yes. You can configure requireToolApproval in your Mastra AI agent. This pauses the workflow and requires a human to approve any tool call, like getpersondetails from ChartHop, before it executes.

Q: How does Mastra AI connect to the ChartHop MCP Server?

You'll use the @mastra/mcp package. Instantiate an MCPClient and pass it the server URL provided by Vinkius. Mastra automatically detects the best transport method.

Q: Can I build a workflow in Mastra AI that compares two ChartHop planning scenarios?

Absolutely. Your agent can call listplanningscenarios, pick two scenarios from the list, and then run your custom logic to compare them. Mastra AI is designed for these kinds of multi-step processes.

Q: What happens if my Mastra AI agent tries to get details for a person who doesn't exist in ChartHop?

The getpersondetails tool will return an error. You can configure your Mastra AI workflow to catch this error and execute a fallback plan, like logging the issue or notifying an admin.

Build resilient HR automation. Use Mastra AI's workflow engine to run multi-step processes against your ChartHop organization data.

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Connect ChartHop MCP to Mastra AI

Create your Vinkius account to connect ChartHop 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 ChartHop with Mastra AI

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Automate Employee Onboarding Checks

Use Mastra AI to build a workflow that runs every time a new employee is added. The agent can use `get_person_details` to check if their manager and team are correctly assigned in ChartHop. If not, the workflow can automatically flag it for review. Mastra's strength is handling failure. If the ChartHop API is temporarily down, the workflow can retry the `get_person_details` call with exponential backoff. You don't have to code that logic; the framework does it for you.

Run Conditional Org Audits

Create a Mastra AI agent that periodically audits your org structure. For example, it could `list_organization_jobs` and check for roles without anyone assigned. If it finds an empty role, it can then use `get_job_details` to see the department and notify the department head. This is where Mastra's conditional branching shines. Your agent's logic can be: IF `get_job_details` shows "department: Engineering", THEN notify the CTO. This lets you build complex, targeted automations on top of your ChartHop data.

Deploy Your ChartHop MCP Server Agent

This MCP Server gives your Mastra agent a reliable set of tools for interacting with ChartHop. You define the workflow—maybe it involves `list_organization_teams` and then `list_organization_people` for each team—and Mastra handles the execution. Once your workflow is defined, you can deploy it with a single command. Mastra AI packages your agent and its connection to the ChartHop MCP Server, ready to run on your cloud infrastructure. It's automation that's built to last.

Setup guide

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

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

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

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Common questions about ChartHop MCP in Mastra AI

Yes. You can configure `requireToolApproval` in your Mastra AI agent. This pauses the workflow and requires a human to approve any tool call, like `get_person_details` from ChartHop, before it executes.

You'll use the `@mastra/mcp` package. Instantiate an `MCPClient` and pass it the server URL provided by Vinkius. Mastra automatically detects the best transport method.

Absolutely. Your agent can call `list_planning_scenarios`, pick two scenarios from the list, and then run your custom logic to compare them. Mastra AI is designed for these kinds of multi-step processes.

The `get_person_details` tool will return an error. You can configure your Mastra AI workflow to catch this error and execute a fallback plan, like logging the issue or notifying an admin.

Vinkius provides a unique token for your Mastra AI agent's connection. The MCP Server only interacts with your ChartHop instance to read people, job, and planning scenario data. Each request is isolated and runs in a sandboxed environment.

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