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How to integrate Everhour MCP with Pydantic AI

Connect Pydantic AI to Everhour MCP. List all clients for this workspace, retrieve expense categories for new report, and more using natural language, with authentication handled for you.

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Everhour is a time tracking and expense management platform for teams and individuals. Gain insight into hours, costs, and budgets for smarter planning and resource allocation.

51 Tools

Introduction

This guide walks you through connecting Everhour to Pydantic AI using the Composio tool router. By the end, you'll have a working Everhour agent that can start timers, clock in and out, manage clients and projects, review timecards and timesheets, and list expenses and invoices through natural language commands.

This guide will help you understand how to give your Pydantic AI agent real control over a Everhour account through Composio's Everhour MCP server.

Before we dive in, let's take a quick look at the key ideas and tools involved.

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TL;DR

Here's what you'll learn:
  • How to set up your Composio API key and User ID
  • How to create a Composio Tool Router session for Everhour
  • How to attach an MCP Server to a Pydantic AI agent
  • How to stream responses and maintain chat history
  • How to build a simple REPL-style chat interface to test your Everhour workflows

What is Pydantic AI?

Pydantic AI is a Python framework for building AI agents with strong typing and validation. It leverages Pydantic's data validation capabilities to create robust, type-safe AI applications.

Key features include:

  • Type Safety: Built on Pydantic for automatic data validation
  • MCP Support: Native support for Model Context Protocol servers
  • Streaming: Built-in support for streaming responses
  • Async First: Designed for async/await patterns

What is the Everhour MCP server, and what's possible with it?

The Everhour MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Everhour account. It provides structured and secure access to your time tracking, client, and expense data, so your agent can perform actions like listing clients, retrieving expenses, managing projects, and fetching user profiles on your behalf.

  • Comprehensive client management: Ask your agent to list, create, or delete clients to keep your workspace organized and up-to-date.
  • Expense tracking and review: Effortlessly retrieve all expenses or list available expense categories to monitor spending and streamline expense management.
  • Project and section insights: Have your agent fetch detailed information about specific projects or sections using their IDs for better resource planning.
  • Personalized user profile access: Enable your agent to fetch the authenticated user's profile for quick access to account details and preferences.
  • Webhook configuration overview: List all configured webhooks to monitor integrations and automate notifications within your Everhour environment.

What is the Composio tool router, and how does it fit here?

What is Composio SDK?

Composio's Composio SDK helps agents find the right tools for a task at runtime. You can plug in multiple toolkits (like Gmail, HubSpot, and GitHub), and the agent will identify the relevant app and action to complete multi-step workflows. This can reduce token usage and improve the reliability of tool calls. Read more here: Getting started with Composio SDK

The tool router generates a secure MCP URL that your agents can access to perform actions.

How the Composio SDK works

The Composio SDK follows a three-phase workflow:

  1. Discovery: Searches for tools matching your task and returns relevant toolkits with their details.
  2. Authentication: Checks for active connections. If missing, creates an auth config and returns a connection URL via Auth Link.
  3. Execution: Executes the action using the authenticated connection.

Step-by-step Guide

Step by step09 STEPS
1

Prerequisites

Before starting, make sure you have:
  • Python 3.9 or higher
  • A Composio account with an active API key
  • Basic familiarity with Python and async programming
2

Getting API Keys for OpenAI and Composio

OpenAI API Key
  • Go to the OpenAI dashboard and create an API key. You'll need credits to use the models, or you can connect to another model provider.
  • Keep the API key safe.
Composio API Key
  • Log in to the Composio dashboard.
  • Navigate to your API settings and generate a new API key.
  • Store this key securely as you'll need it for authentication.
3

Install dependencies

bash
pip install composio pydantic-ai python-dotenv

Install the required libraries.

What's happening:

  • composio connects your agent to external SaaS tools like Everhour
  • pydantic-ai lets you create structured AI agents with tool support
  • python-dotenv loads your environment variables securely from a .env file
4

Set up environment variables

bash
COMPOSIO_API_KEY=your_composio_api_key_here
USER_ID=your_user_id_here
OPENAI_API_KEY=your_openai_api_key

Create a .env file in your project root.

What's happening:

  • COMPOSIO_API_KEY authenticates your agent to Composio's API
  • USER_ID associates your session with your account for secure tool access
  • OPENAI_API_KEY to access OpenAI LLMs
5

Import dependencies

python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerStreamableHTTP

load_dotenv()
What's happening:
  • We load environment variables and import required modules
  • Composio manages connections to Everhour
  • MCPServerStreamableHTTP connects to the Everhour MCP server endpoint
  • Agent from Pydantic AI lets you define and run the AI assistant
6

Create a Tool Router Session

python
async def main():
    api_key = os.getenv("COMPOSIO_API_KEY")
    user_id = os.getenv("USER_ID")
    if not api_key or not user_id:
        raise RuntimeError("Set COMPOSIO_API_KEY and USER_ID in your environment")

    # Create a Composio Tool Router session for Everhour
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["everhour"],
    )
    url = session.mcp.url
    if not url:
        raise ValueError("Composio session did not return an MCP URL")
What's happening:
  • We're creating a Tool Router session that gives your agent access to Everhour tools
  • The create method takes the user ID and specifies which toolkits should be available
  • The returned session.mcp.url is the MCP server URL that your agent will use
7

Initialize the Pydantic AI Agent

python
# Attach the MCP server to a Pydantic AI Agent
everhour_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
agent = Agent(
    "openai:gpt-5",
    toolsets=[everhour_mcp],
    instructions=(
        "You are a Everhour assistant. Use Everhour tools to help users "
        "with their requests. Ask clarifying questions when needed."
    ),
)
What's happening:
  • The MCP client connects to the Everhour endpoint
  • The agent uses GPT-5 to interpret user commands and perform Everhour operations
  • The instructions field defines the agent's role and behavior
8

Build the chat interface

python
# Simple REPL with message history
history = []
print("Chat started! Type 'exit' or 'quit' to end.\n")
print("Try asking the agent to help you with Everhour.\n")

while True:
    user_input = input("You: ").strip()
    if user_input.lower() in {"exit", "quit", "bye"}:
        print("\nGoodbye!")
        break
    if not user_input:
        continue

    print("\nAgent is thinking...\n", flush=True)

    async with agent.run_stream(user_input, message_history=history) as stream_result:
        collected_text = ""
        async for chunk in stream_result.stream_output():
            text_piece = None
            if isinstance(chunk, str):
                text_piece = chunk
            elif hasattr(chunk, "delta") and isinstance(chunk.delta, str):
                text_piece = chunk.delta
            elif hasattr(chunk, "text"):
                text_piece = chunk.text
            if text_piece:
                collected_text += text_piece
        result = stream_result

    print(f"Agent: {collected_text}\n")
    history = result.all_messages()
What's happening:
  • The agent reads input from the terminal and streams its response
  • Everhour API calls happen automatically under the hood
  • The model keeps conversation history to maintain context across turns
9

Run the application

python
if __name__ == "__main__":
    asyncio.run(main())
What's happening:
  • The asyncio loop launches the agent and keeps it running until you exit

Complete Code

Here's the complete code to get you started with Everhour and Pydantic AI:

python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerStreamableHTTP

load_dotenv()

async def main():
    api_key = os.getenv("COMPOSIO_API_KEY")
    user_id = os.getenv("USER_ID")
    if not api_key or not user_id:
        raise RuntimeError("Set COMPOSIO_API_KEY and USER_ID in your environment")

    # Create a Composio Tool Router session for Everhour
    composio = Composio(api_key=api_key)
    session = composio.create(
        user_id=user_id,
        toolkits=["everhour"],
    )
    url = session.mcp.url
    if not url:
        raise ValueError("Composio session did not return an MCP URL")

    # Attach the MCP server to a Pydantic AI Agent
    everhour_mcp = MCPServerStreamableHTTP(url, headers={"x-api-key": COMPOSIO_API_KEY})
    agent = Agent(
        "openai:gpt-5",
        toolsets=[everhour_mcp],
        instructions=(
            "You are a Everhour assistant. Use Everhour tools to help users "
            "with their requests. Ask clarifying questions when needed."
        ),
    )

    # Simple REPL with message history
    history = []
    print("Chat started! Type 'exit' or 'quit' to end.\n")
    print("Try asking the agent to help you with Everhour.\n")

    while True:
        user_input = input("You: ").strip()
        if user_input.lower() in {"exit", "quit", "bye"}:
            print("\nGoodbye!")
            break
        if not user_input:
            continue

        print("\nAgent is thinking...\n", flush=True)

        async with agent.run_stream(user_input, message_history=history) as stream_result:
            collected_text = ""
            async for chunk in stream_result.stream_output():
                text_piece = None
                if isinstance(chunk, str):
                    text_piece = chunk
                elif hasattr(chunk, "delta") and isinstance(chunk.delta, str):
                    text_piece = chunk.delta
                elif hasattr(chunk, "text"):
                    text_piece = chunk.text
                if text_piece:
                    collected_text += text_piece
            result = stream_result

        print(f"Agent: {collected_text}\n")
        history = result.all_messages()

if __name__ == "__main__":
    asyncio.run(main())

Conclusion

You've built a Pydantic AI agent that can interact with Everhour through Composio's Tool Router. With this setup, your agent can perform real Everhour actions through natural language. You can extend this further by:
  • Adding other toolkits like Gmail, HubSpot, or Salesforce
  • Building a web-based chat interface around this agent
  • Using multiple MCP endpoints to enable cross-app workflows (for example, Gmail + Everhour for workflow automation)
This architecture makes your AI agent "agent-native", able to securely use APIs in a unified, composable way without custom integrations.
TOOLS

Supported Tools

Every Everhour action and event your agent gets out of the box.

Add Time

Add or upsert tracked time for a task, user, and date.

Create Client

Creates a new client in Everhour for tracking billable work, invoicing, and project organization.

Delete a client

Tool to delete a client.

List Clients

Retrieves all clients in the Everhour workspace.

Clock In User

Tool to clock in a user for time tracking.

Clock Out User

Tool to clock out a user for time tracking.

Create Webhook

Tool to create a new webhook for event notifications in Everhour.

Delete Webhook

Tool to delete a webhook.

Delete a timecard

Delete a user's timecard for a specific date in Everhour.

Delete Time Record

Remove tracked time by record ID while retaining its history trail.

Discard Timesheet Approval

Withdraw the authenticated user's previously submitted timesheet approval request for a week.

List Expenses

List expense records logged by the authenticated user in Everhour.

Get Client by ID

Tool to retrieve a specific client by ID.

Get Current Timer

Get the authenticated Everhour user's current timer state.

Get Project

Tool to retrieve a specific project.

Get Projects Report

Return aggregated Everhour project time and financial metrics.

Get Section

Retrieve details of a specific section by its ID.

Get Task

Retrieve one native or integration-backed Everhour task by ID.

Get Task Time Records

Lists tracked-time records for an Everhour task with date and page filters.

Get Timecard

Tool to retrieve a specific timecard for a user on a date.

Get Authenticated User Profile

Tool to retrieve profile information of the authenticated user.

Get User Time Records

List one Everhour user's tracked-time records.

Get Webhook

Retrieve details of a specific webhook by its ID.

List Expense Categories

Lists all expense categories available in your Everhour account.

List Webhooks

List webhooks created with the Everhour API key, including their target URLs, subscribed events, active status, and timestamps.

List Invoices

Retrieves all invoices from your Everhour workspace.

List projects

Lists Everhour projects accessible to the authenticated user, with optional name, status, platform, and result-limit parameters.

List Project Tasks

List tasks in an Everhour project with optional filters and pagination.

List Project Time Records

List tracked-time records for an Everhour project.

List Sections

Lists all sections within a specific Everhour project.

List Tags

List all tags in the Everhour workspace.

List Team Members

Retrieves team members in the authenticated Everhour workspace, including IDs, names, roles, and statuses.

List Teams

Retrieves information about the authenticated team/workspace in Everhour.

List Team Time Records

List team time records for a date range; without dates, return only today.

List Timecards

Retrieves team timecards, including clock-in/out data and work hours, with optional date filtering.

List User Timecards

Retrieves a specific user's timecards, including clock-in/out data and work hours, with optional date filtering.

List User Timesheets

Tool to retrieve timesheets for a specific user.

Create Project

Tool to create a new project in Everhour.

Delete a project

Permanently deletes an Everhour project by ID, including its tasks and associated time records.

Request Timesheet Approval

Tool to request approval for a timesheet or approve a week (for admins).

Search Tasks

Search accessible Everhour tasks, optionally including closed tasks.

Create Section

Tool to create a new section in a project.

Delete a section

Tool to delete a section.

Create Task

Creates a new task in an Everhour project.

Start Timer

Tool to start a new timer for a task.

Update Client

Tool to update an existing client in Everhour.

Update Expense

Partially update an existing Everhour expense.

Update an existing project

Updates an existing Everhour project's settings by project ID, including its name or notes.

Update Timecard

Update or create a user's timecard for a specific date, setting clock-in, clock-out, or break duration.

Update Time Record

Update an existing tracked-time record by its record ID.

Update Webhook

Update an existing Everhour webhook's subscribed events or project filter.

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OpenClaw

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Atomic Agent

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Google ADK logo

Google ADK

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LangChain logo

LangChain

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Vercel AI SDK logo

Vercel AI SDK

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Mastra AI logo

Mastra AI

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LlamaIndex logo

LlamaIndex

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CrewAI logo

CrewAI

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AutoGen logo

AutoGen

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FAQ

Frequently asked questions

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