Tempo MCP Server
by ivelin-web
The Tempo MCP Server is a Model Context Protocol server designed to manage Tempo worklogs within Jira. It provides tools for tracking time and managing worklogs through Tempo's API, accessible via MCP-compatible clients like Claude and Cursor.
Last updated: N/A
What is Tempo MCP Server?
The Tempo MCP Server is a server that implements the Model Context Protocol to facilitate interaction with Tempo's API for managing Jira worklogs. It allows users to retrieve, create, edit, and delete worklogs through MCP-compatible clients.
How to use Tempo MCP Server?
The server can be used either by running it directly with NPX or by cloning the repository and running it locally. Configuration involves setting environment variables for Tempo and Jira API tokens, email, and base URL, and then configuring your MCP client (e.g., Claude Desktop) to connect to the server.
Key features of Tempo MCP Server
Retrieve Worklogs
Create Worklog
Bulk Create Worklogs
Edit Worklog
Delete Worklog
Use cases of Tempo MCP Server
Tracking time spent on Jira issues
Logging work hours against specific tasks
Managing worklogs through AI assistants
Automating worklog creation and updates
Integrating Tempo worklogs with other applications via MCP
FAQ from Tempo MCP Server
What is an MCP server?
What is an MCP server?
An MCP (Model Context Protocol) server allows applications to interact with external services or data sources in a standardized way.
What are the system requirements?
What are the system requirements?
Node.js 18+, a Jira Cloud instance, a Tempo API token, and a Jira API token are required.
How do I get Tempo and Jira API tokens?
How do I get Tempo and Jira API tokens?
Tempo API tokens can be created in Tempo settings, and Jira API tokens can be created in your Atlassian account settings.
How do I configure the server with Claude Desktop?
How do I configure the server with Claude Desktop?
You need to modify the claude_desktop_config.json file to include the server configuration, specifying the command, arguments, and environment variables.
What if I encounter issues?
What if I encounter issues?
Check that all environment variables are properly set, verify your API tokens have the correct permissions, check the console output for error messages, and try running with the inspector.