How to Use the Clockify MCP in Mastra AI

Q: Can Mastra AI automatically retry failed Clockify time entries?

Yes. The framework's workflow engine automatically retries addnewtimeentry if it encounters rate limits or network glitches.

Q: How do I fetch workspace clients using a Mastra workflow?

Your agent calls listworkspaceclients as a step in your workflow graph. You can then pipe those client details directly into down-stream billing actions.

Build automated billing and tracking workflows that sync Clockify data using Mastra AI agents.

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Connect Clockify MCP to Mastra AI

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Setup Clockify with Mastra AI

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Automated error recovery for time logging

Time tracking shouldn't break when an API hiccup occurs. Mastra AI uses built-in exponential backoff to retry `add_new_time_entry` if the initial request fails. If the Clockify workspace is locked, the Mastra workflow branches. The agent can automatically query `list_clockify_workspaces` to find an alternative target or alert an admin.

Human-in-the-loop validation for Mastra AI agents

Prevent agents from stopping the wrong timer. By enabling `requireToolApproval` on this MCP Server, your Mastra AI workflow pauses before executing `stop_current_timer`. The developer gets a clean approval hook. Once you click approve, the Mastra agent resumes and stops the Clockify timer, keeping your records clean.

Multi-step workspace auditing

Audit your team's hours without manual checking. The Mastra AI framework runs complex checks by chaining `list_workspace_users` and `list_user_time_entries` sequentially. It processes the data in steps. The Mastra agent compiles the list of Clockify users, checks for missing logs, and writes the summary to your database in one run.

Setup guide

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

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

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

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Common questions about Clockify MCP in Mastra AI

Instantiate the MCP client with the server URL and spread the tools into your agent config. Your agent can immediately call `list_workspace_projects` during workflow runs.

Yes. The framework's workflow engine automatically retries `add_new_time_entry` if it encounters rate limits or network glitches.

It does. You can configure Mastra AI to require manual confirmation before the MCP client runs `stop_current_timer`, protecting active sessions.

Your agent calls `list_workspace_clients` as a step in your workflow graph. You can then pipe those client details directly into down-stream billing actions.

Your credentials reside strictly in your local environment variables. The agent fetches profile details using `get_my_clockify_profile` entirely within your private cloud deployment, keeping them out of third-party logs.

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