How to Use the Everhour Time Tracking MCP in OpenAI Agents SDK

Q: Does Everhour Time Tracking support multi-agent handoffs in OpenAI Agents SDK?

It works perfectly with handoffs. One agent can run listbillingclients to find a client, then pass that exact context to a secondary agent handling budget analysis.

Connect the Everhour Time Tracking MCP Server to the OpenAI Agents SDK and let your production agents log hours automatically.

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Connect Everhour Time Tracking MCP to OpenAI Agents SDK

Create your Vinkius account to connect Everhour Time Tracking to OpenAI Agents SDK 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 Everhour Time Tracking with OpenAI Agents SDK

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Claude

Perplexity

Automate time audits with OpenAI Agents SDK

The `quick_time_tracking_audit` tool retrieves a high-level summary of recent time entries and active projects immediately. You build the guardrails in Python, and the agent fetches the exact hours your team logged this week. When you need to check who is actually working, `list_team_time_records` lists time records for the team within a specific date range. Your agent hands this off to an analysis node, giving you clear visibility without ever opening the Everhour dashboard.

Monitor project budgets via MCP Server

The `list_projects_within_budget` tool identifies projects that are currently within their assigned time or monetary budget. Your deployed agent checks this endpoint daily and triggers alerts if a project nears the red line. For deeper financial checks, `get_project_detailed_data` gets detailed settings and budget information for a specific project. The agent reads the exact numbers and logs them into your production tracing system so you know exactly where the money went.

Manage tasks and active timers

`get_currently_running_timer` retrieves the task and start time for any currently active timer. If someone forgets to stop their clock, your agent spots it and fires off a reminder. You also get `list_project_tasks` to list all tasks within a specific project. Combine this with `list_organization_team_members` to have the agent map out who is assigned to what, keeping your workforce aligned.

Setup guide

Set up Everhour Time Tracking MCP in OpenAI Agents SDK

Prerequisites

Python 3.10+ installed
openai-agents package (pip install openai-agents)
Active Vinkius subscription with a valid endpoint token

1

Install the SDK

Run pip install openai-agents to install the OpenAI Agents SDK. The MCP integration is built-in — no extra dependencies needed.
2

Connect via SSE transport

Use MCPServerSse with your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. The SDK auto-discovers all Everhour Time Tracking tools at runtime.
3

Create your Agent

Pass the MCP to Agent(mcp_servers=[server]). The agent receives Everhour Time Tracking tools as native definitions — JSON schemas resolve automatically.
4

Run the agent

Call Runner.run(agent, prompt) to execute. The agent invokes the appropriate Everhour Time Tracking tools and returns structured results. Copy the full example on the right to get started.

agent.py

import asyncio
from agents import Agent, Runner
from agents.mcp import MCPServerSse

async def main():
    async with MCPServerSse(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ) as server:
        agent = Agent(
            name="Everhour Time Tracking Agent",
            instructions="You have access to Everhour Time Tracking tools.",
            mcp_servers=[server],
        )
        result = await Runner.run(agent, "List recent transactions")
        print(result.final_output)

asyncio.run(main())

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Common questions about Everhour Time Tracking MCP in OpenAI Agents SDK

Install the package via pip install openai-agents. Create an MCPServerStreamableHttp instance with your endpoint URL, and pass it to the Agent constructor using mcp_servers=[server].

Yes. Set cacheToolsList=True when configuring your server parameters. This prevents the agent from fetching the tool list on every single run.

It works perfectly with handoffs. One agent can run list_billing_clients to find a client, then pass that exact context to a secondary agent handling budget analysis.

The MCP server returns an error code, which the SDK catches. Your built-in guardrails handle the failure before the agent tries to execute a broken action.

This server reads your raw time entries and budget limits directly from the API. The connection uses a secure V8 Isolate Sandbox, ensuring your financial data stays isolated during the session and vanishes when the run finishes.

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