# How to integrate Desktime MCP with Autogen ```json { "title": "How to integrate Desktime MCP with Autogen", "toolkit": "Desktime", "toolkit_slug": "desktime", "framework": "AutoGen", "framework_slug": "autogen", "url": "https://composio.dev/toolkits/desktime/framework/autogen", "markdown_url": "https://composio.dev/toolkits/desktime/framework/autogen.md", "updated_at": "2026-05-12T10:08:32.821Z" } ``` ## Introduction This guide walks you through connecting Desktime to AutoGen using the Composio tool router. By the end, you'll have a working Desktime agent that can list all active projects with tasks, get today's time tracked for each employee, start timing work on 'client website redesign' through natural language commands. This guide will help you understand how to give your AutoGen agent real control over a Desktime account through Composio's Desktime MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved. ## Also integrate Desktime with - [OpenAI Agents SDK](https://composio.dev/toolkits/desktime/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/desktime/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/desktime/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/desktime/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/desktime/framework/codex) - [OpenClaw](https://composio.dev/toolkits/desktime/framework/openclaw) - [Hermes](https://composio.dev/toolkits/desktime/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/desktime/framework/cli) - [Google ADK](https://composio.dev/toolkits/desktime/framework/google-adk) - [LangChain](https://composio.dev/toolkits/desktime/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/desktime/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/desktime/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/desktime/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/desktime/framework/crew-ai) ## TL;DR Here's what you'll learn: - Get and set up your OpenAI and Composio API keys - Install the required dependencies for Autogen and Composio - Initialize Composio and create a Tool Router session for Desktime - Wire that MCP URL into Autogen using McpWorkbench and StreamableHttpServerParams - Configure an Autogen AssistantAgent that can call Desktime tools - Run a live chat loop where you ask the agent to perform Desktime operations ## What is AutoGen? Autogen is a framework for building multi-agent conversational AI systems from Microsoft. It enables you to create agents that can collaborate, use tools, and maintain complex workflows. Key features include: - Multi-Agent Systems: Build collaborative agent workflows - MCP Workbench: Native support for Model Context Protocol tools - Streaming HTTP: Connect to external services through streamable HTTP - AssistantAgent: Pre-built agent class for tool-using assistants ## What is the Desktime MCP server, and what's possible with it? The Desktime MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Desktime account. It provides structured and secure access to your time tracking, project management, and productivity data, so your agent can perform actions like tracking work hours, managing projects, analyzing employee productivity, and reporting on team activities on your behalf. - Automated project and task management: Create new projects, assign optional starter tasks, and retrieve a comprehensive list of active company projects for streamlined organization. - Employee and company insights: Instantly fetch detailed company account info, list all employees with their roles and statuses, and access individual employee data for better workforce visibility. - Time tracking control: Start or stop tracking work on specific projects and tasks, enabling hands-free, accurate logging of work sessions and project contributions. - Productivity and app usage analysis: Retrieve employee project assignments and application usage data to monitor work habits and identify productivity trends across your team. - API health and connectivity checks: Use built-in API ping tools to verify Desktime API availability and ensure uninterrupted agent operations. ## Supported Tools | Tool slug | Name | Description | |---|---|---| | `DESKTIME_CREATE_PROJECT_WITH_TASK` | Create Project with Optional Task | Tool to create a new project with an optional initial task. Use when you need to set up a new DeskTime project programmatically. | | `DESKTIME_GET_ACCOUNT_DETAILS` | Get Account Details | Retrieves company account configuration including work schedule and timezone settings. This action fetches company-level information such as company name, work hours (start/end times), work duration, working days configuration, time tracking hours, and timezone. No parameters are required. This is useful for understanding the company's work schedule configuration and timezone settings. | | `DESKTIME_GET_ALL_COMPANY_EMPLOYEES` | Get All Company Employees | Tool to list all employees in the company, including their roles and statuses. Use after confirming valid credentials to fetch the organization’s roster. | | `DESKTIME_GET_EMPLOYEE` | Get Employee | Tool to retrieve information about a single employee including user info, work settings, and tracking data for a specific date. Use when you need detailed information for one employee. Returns data for the currently logged-in user if no employee ID is specified. | | `DESKTIME_GET_EMPLOYEE_APPS` | Get Employee Apps | Retrieves employee tracking data including tracked apps for a specific date. Returns data for the currently logged-in user if no employee ID is specified. Use this action to view detailed application usage and productivity data for an employee. | | `DESKTIME_GET_EMPLOYEE_PROJECTS` | Get Employee Projects | Retrieves comprehensive employee project tracking data including project assignments, time tracking metrics, work hours, and productivity statistics for a specific employee and date. Returns detailed information about: - Employee profile (ID, name, email, group) - Time tracking metrics (online time, productive time, efficiency) - Work schedule (work start/end times, timezone) - Active project details (current project and task being worked on) - Projects list (all projects tracked on the specified date with durations) - Employee status indicators (online, arrived, left, late) Use this action when you need to: - View an employee's project assignments and tracking data - Check time spent on specific projects by an employee - Monitor employee productivity and work hours - Retrieve historical project tracking data for a specific date Both parameters are optional - defaults to current API-key user and today's date. | | `DESKTIME_GET_EMPLOYEE_PROJECTS_AND_APPS` | Get Employee Basic Data | Retrieve an employee's basic information and daily tracking statistics from DeskTime. Returns employee profile data, work hours, productivity metrics, attendance status, and currently active project. Use this when you need employee time tracking data for a specific date (defaults to today). Note: For detailed project/app usage breakdowns, use the dedicated Get Employee Projects action. | | `DESKTIME_GET_PROJECTS_LIST` | Get Projects List | Tool to retrieve all active projects for the company, including related tasks. Use when you need projects overview after authentication. | | `DESKTIME_PING_REQUEST` | Ping DeskTime API | Tool to check the API's availability and confirm the service is operational. Use when you need to verify that the DeskTime API is reachable and responsive. | | `DESKTIME_START_PROJECT_TASK` | Start Project Task | Starts time tracking for a specified project and optional task in DeskTime. This action begins recording time against the specified project. If a task name is provided, time is also tracked at the task level within that project. Both projects and tasks are created automatically if they don't already exist in the DeskTime account. Use this action when a user wants to: - Begin working on a project and track time - Start a specific task within a project - Switch time tracking to a different project or task Note: Only one project/task can be tracked at a time per user. Starting a new project automatically stops tracking on any previously active project. | | `DESKTIME_STOP_PROJECT_TASK` | Stop Project Task | Tool to stop tracking time for a specified project and optional task. Use when you have finished work and need to record end time. | ## Supported Triggers None listed. ## Creating MCP Server - Stand-alone vs Composio SDK The Desktime MCP server is an implementation of the Model Context Protocol that connects your AI agents and assistants directly to Desktime. Instead of manually wiring Desktime APIs, OAuth, and scopes yourself, you get a structured, tool-based interface that an LLM can call safely. With Composio's managed implementation, you don't have to create your own developer app. For production, if you're building an end product, we recommend using your own credentials. The managed server helps you prototype fast and go from 0-1 faster. ## Step-by-step Guide ### 1. Prerequisites You will need: - A Composio API key - An OpenAI API key (used by Autogen's OpenAIChatCompletionClient) - A Desktime account you can connect to Composio - Some basic familiarity with Autogen and Python async ### 1. Getting API Keys for OpenAI and Composio OpenAI API Key - Go to the [OpenAI dashboard](https://platform.openai.com/settings/organization/api-keys) 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](https://dashboard.composio.dev?utm_source=toolkits&utm_medium=framework_docs). - Navigate to your API settings and generate a new API key. - Store this key securely as you'll need it for authentication. ### 2. Install dependencies Install Composio, Autogen extensions, and dotenv. What's happening: - composio connects your agent to Desktime via MCP - autogen-agentchat provides the AssistantAgent class - autogen-ext-openai provides the OpenAI model client - autogen-ext-tools provides MCP workbench support ```bash pip install composio python-dotenv pip install autogen-agentchat autogen-ext-openai autogen-ext-tools ``` ### 3. Set up environment variables Create a .env file in your project folder. What's happening: - COMPOSIO_API_KEY is required to talk to Composio - OPENAI_API_KEY is used by Autogen's OpenAI client - USER_ID is how Composio identifies which user's Desktime connections to use ```bash COMPOSIO_API_KEY=your-composio-api-key OPENAI_API_KEY=your-openai-api-key USER_ID=your-user-identifier@example.com ``` ### 4. Import dependencies and create Tool Router session What's happening: - load_dotenv() reads your .env file - Composio(api_key=...) initializes the SDK - create(...) creates a Tool Router session that exposes Desktime tools - session.mcp.url is the MCP endpoint that Autogen will connect to ```python import asyncio import os from dotenv import load_dotenv from composio import Composio from autogen_agentchat.agents import AssistantAgent from autogen_ext.models.openai import OpenAIChatCompletionClient from autogen_ext.tools.mcp import McpWorkbench, StreamableHttpServerParams load_dotenv() async def main(): # Initialize Composio and create a Desktime session composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY")) session = composio.create( user_id=os.getenv("USER_ID"), toolkits=["desktime"] ) url = session.mcp.url ``` ### 5. Configure MCP parameters for Autogen Autogen expects parameters describing how to talk to the MCP server. That is what StreamableHttpServerParams is for. What's happening: - url points to the Tool Router MCP endpoint from Composio - timeout is the HTTP timeout for requests - sse_read_timeout controls how long to wait when streaming responses - terminate_on_close=True cleans up the MCP server process when the workbench is closed ```python # Configure MCP server parameters for Streamable HTTP server_params = StreamableHttpServerParams( url=url, timeout=30.0, sse_read_timeout=300.0, terminate_on_close=True, headers={"x-api-key": os.getenv("COMPOSIO_API_KEY")} ) ``` ### 6. Create the model client and agent What's happening: - OpenAIChatCompletionClient wraps the OpenAI model for Autogen - McpWorkbench connects the agent to the MCP tools - AssistantAgent is configured with the Desktime tools from the workbench ```python # Create model client model_client = OpenAIChatCompletionClient( model="gpt-5", api_key=os.getenv("OPENAI_API_KEY") ) # Use McpWorkbench as context manager async with McpWorkbench(server_params) as workbench: # Create Desktime assistant agent with MCP tools agent = AssistantAgent( name="desktime_assistant", description="An AI assistant that helps with Desktime operations.", model_client=model_client, workbench=workbench, model_client_stream=True, max_tool_iterations=10 ) ``` ### 7. Run the interactive chat loop What's happening: - The script prompts you in a loop with You: - Autogen passes your input to the model, which decides which Desktime tools to call via MCP - agent.run_stream(...) yields streaming messages as the agent thinks and calls tools - Typing exit, quit, or bye ends the loop ```python print("Chat started! Type 'exit' or 'quit' to end the conversation.\n") print("Ask any Desktime related question or task to the agent.\n") # Conversation loop 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") # Run the agent with streaming try: response_text = "" async for message in agent.run_stream(task=user_input): if hasattr(message, "content") and message.content: response_text = message.content # Print the final response if response_text: print(f"Agent: {response_text}\n") else: print("Agent: I encountered an issue processing your request.\n") except Exception as e: print(f"Agent: Sorry, I encountered an error: {str(e)}\n") ``` ## Complete Code ```python import asyncio import os from dotenv import load_dotenv from composio import Composio from autogen_agentchat.agents import AssistantAgent from autogen_ext.models.openai import OpenAIChatCompletionClient from autogen_ext.tools.mcp import McpWorkbench, StreamableHttpServerParams load_dotenv() async def main(): # Initialize Composio and create a Desktime session composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY")) session = composio.create( user_id=os.getenv("USER_ID"), toolkits=["desktime"] ) url = session.mcp.url # Configure MCP server parameters for Streamable HTTP server_params = StreamableHttpServerParams( url=url, timeout=30.0, sse_read_timeout=300.0, terminate_on_close=True, headers={"x-api-key": os.getenv("COMPOSIO_API_KEY")} ) # Create model client model_client = OpenAIChatCompletionClient( model="gpt-5", api_key=os.getenv("OPENAI_API_KEY") ) # Use McpWorkbench as context manager async with McpWorkbench(server_params) as workbench: # Create Desktime assistant agent with MCP tools agent = AssistantAgent( name="desktime_assistant", description="An AI assistant that helps with Desktime operations.", model_client=model_client, workbench=workbench, model_client_stream=True, max_tool_iterations=10 ) print("Chat started! Type 'exit' or 'quit' to end the conversation.\n") print("Ask any Desktime related question or task to the agent.\n") # Conversation loop 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") # Run the agent with streaming try: response_text = "" async for message in agent.run_stream(task=user_input): if hasattr(message, 'content') and message.content: response_text = message.content # Print the final response if response_text: print(f"Agent: {response_text}\n") else: print("Agent: I encountered an issue processing your request.\n") except Exception as e: print(f"Agent: Sorry, I encountered an error: {str(e)}\n") if __name__ == "__main__": asyncio.run(main()) ``` ## Conclusion You now have an Autogen assistant wired into Desktime through Composio's Tool Router and MCP. From here you can: - Add more toolkits to the toolkits list, for example notion or hubspot - Refine the agent description to point it at specific workflows - Wrap this script behind a UI, Slack bot, or internal tool Once the pattern is clear for Desktime, you can reuse the same structure for other MCP-enabled apps with minimal code changes. ## How to build Desktime MCP Agent with another framework - [OpenAI Agents SDK](https://composio.dev/toolkits/desktime/framework/open-ai-agents-sdk) - [Claude Agent SDK](https://composio.dev/toolkits/desktime/framework/claude-agents-sdk) - [Claude Code](https://composio.dev/toolkits/desktime/framework/claude-code) - [Claude Cowork](https://composio.dev/toolkits/desktime/framework/claude-cowork) - [Codex](https://composio.dev/toolkits/desktime/framework/codex) - [OpenClaw](https://composio.dev/toolkits/desktime/framework/openclaw) - [Hermes](https://composio.dev/toolkits/desktime/framework/hermes-agent) - [CLI](https://composio.dev/toolkits/desktime/framework/cli) - [Google ADK](https://composio.dev/toolkits/desktime/framework/google-adk) - [LangChain](https://composio.dev/toolkits/desktime/framework/langchain) - [Vercel AI SDK](https://composio.dev/toolkits/desktime/framework/ai-sdk) - [Mastra AI](https://composio.dev/toolkits/desktime/framework/mastra-ai) - [LlamaIndex](https://composio.dev/toolkits/desktime/framework/llama-index) - [CrewAI](https://composio.dev/toolkits/desktime/framework/crew-ai) ## Related Toolkits - [Google Sheets](https://composio.dev/toolkits/googlesheets) - Google Sheets is a cloud-based spreadsheet tool for real-time collaboration and data analysis. It lets teams work together from anywhere, updating information instantly. - [Notion](https://composio.dev/toolkits/notion) - Notion is a collaborative workspace for notes, docs, wikis, and tasks. It streamlines team knowledge, project tracking, and workflow customization in one place. - [Airtable](https://composio.dev/toolkits/airtable) - Airtable combines the flexibility of spreadsheets with the power of a database for easy project and data management. Teams use Airtable to organize, track, and collaborate with custom views and automations. - [Asana](https://composio.dev/toolkits/asana) - Asana is a collaborative work management platform for teams to organize and track projects. It streamlines teamwork, boosts productivity, and keeps everyone aligned on goals. - [Google Tasks](https://composio.dev/toolkits/googletasks) - Google Tasks is a to-do list and task management tool integrated into Gmail and Google Calendar. It helps you organize, track, and complete tasks across your Google ecosystem. - [Linear](https://composio.dev/toolkits/linear) - Linear is a modern issue tracking and project planning tool for fast-moving teams. It helps streamline workflows, organize projects, and boost productivity. - [Jira](https://composio.dev/toolkits/jira) - Jira is Atlassian’s platform for bug tracking, issue tracking, and agile project management. It helps teams organize work, prioritize tasks, and deliver projects efficiently. - [Clickup](https://composio.dev/toolkits/clickup) - ClickUp is an all-in-one productivity platform for managing tasks, docs, goals, and team collaboration. It streamlines project workflows so teams can work smarter and stay organized in one place. - [Monday](https://composio.dev/toolkits/monday) - Monday.com is a customizable work management platform for project planning and collaboration. It helps teams organize tasks, automate workflows, and track progress in real time. - [Addressfinder](https://composio.dev/toolkits/addressfinder) - Addressfinder is a data quality platform for verifying addresses, emails, and phone numbers. It helps you ensure accurate customer and contact data every time. - [Agiled](https://composio.dev/toolkits/agiled) - Agiled is an all-in-one business management platform for CRM, projects, and finance. It helps you streamline workflows, consolidate client data, and manage business processes in one place. - [Ascora](https://composio.dev/toolkits/ascora) - Ascora is a cloud-based field service management platform for service businesses. It streamlines scheduling, invoicing, and customer operations in one place. - [Basecamp](https://composio.dev/toolkits/basecamp) - Basecamp is a project management and team collaboration tool by 37signals. It helps teams organize tasks, share files, and communicate efficiently in one place. - [Beeminder](https://composio.dev/toolkits/beeminder) - Beeminder is an online goal-tracking platform that uses monetary pledges to keep you motivated. Stay accountable and hit your targets with real financial incentives. - [Boxhero](https://composio.dev/toolkits/boxhero) - Boxhero is a cloud-based inventory management platform for SMBs, offering real-time updates, barcode scanning, and team collaboration. It helps businesses streamline stock tracking and analytics for smarter inventory decisions. - [Breathe HR](https://composio.dev/toolkits/breathehr) - Breathe HR is cloud-based HR software for SMEs to manage employee data, absences, and performance. It simplifies HR admin, making it easy to keep employee records accurate and up to date. - [Breeze](https://composio.dev/toolkits/breeze) - Breeze is a project management platform designed to help teams plan, track, and collaborate on projects. It streamlines workflows and keeps everyone on the same page. - [Bugherd](https://composio.dev/toolkits/bugherd) - Bugherd is a visual feedback and bug tracking tool for websites. It helps teams and clients report website issues directly on live sites for faster fixes. - [Canny](https://composio.dev/toolkits/canny) - Canny is a platform for managing customer feedback and feature requests. It helps teams prioritize product decisions based on real user insights. - [Chmeetings](https://composio.dev/toolkits/chmeetings) - Chmeetings is a church management platform for events, members, donations, and volunteers. It streamlines church operations and improves community engagement. ## Frequently Asked Questions ### What are the differences in Tool Router MCP and Desktime MCP? With a standalone Desktime MCP server, the agents and LLMs can only access a fixed set of Desktime tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Desktime and many other apps based on the task at hand, all through a single MCP endpoint. ### Can I use Tool Router MCP with Autogen? Yes, you can. Autogen fully supports MCP integration. You get structured tool calling, message history handling, and model orchestration while Tool Router takes care of discovering and serving the right Desktime tools. ### Can I manage the permissions and scopes for Desktime while using Tool Router? Yes, absolutely. You can configure which Desktime scopes and actions are allowed when connecting your account to Composio. You can also bring your own OAuth credentials or API configuration so you keep full control over what the agent can do. ### How safe is my data with Composio Tool Router? All sensitive data such as tokens, keys, and configuration is fully encrypted at rest and in transit. Composio is SOC 2 Type 2 compliant and follows strict security practices so your Desktime data and credentials are handled as safely as possible. --- [See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)