⏰ Tempo Filler MCP Server

A Model Context Protocol (MCP) server for managing Tempo worklogs in JIRA. This server enables AI assistants to interact with Tempo's time tracking system, allowing for worklog retrieval, creation, bulk operations, and management.

🚀 Quick Start

Install in VS Code

Install in Claude Desktop

1. Download the MCP bundle first

2. Then go to settings → extensions and drag the file there to install.

3. Fill up the Tempo Base URL and PAT in the environment variables section.

4. Don't forget to enable it.

Install Manually on your favorite AI Assistant

Related MCP server: Jira MCP Server

💬 Real Usage Example

Here's an actual conversation showing how natural and powerful the interaction can be:

👤 User:

fill my october hours with the same project from september

🤖 AI Assistant:

I'll help you fill your October hours with the same project from September. Let me first check your September worklogs to understand the pattern, then fill October accordingly.

Perfect! I can see you worked on PROJ-1234 (Tempo MCP Server) for 200 hours in September. Now let me get your October work schedule to see which days need to be filled.

Excellent! I now have all the information needed. Let me fill all 23 working days in October with the Tempo MCP Server project using 8 hours per working day.

✅ October Hours Filled Successfully

• Project: PROJ-1234 (Tempo MCP Server)

• Total Hours: 184 hours across 23 working days

• Hours per Day: 8 hours (matching your standard work schedule)

• Status: All entries created successfully with no failures

Tool Calls Made:

• get_worklogs - Retrieved September pattern (PROJ-1234, 200h)

• get_schedule - Found 23 working days in October (184h capacity)

• bulk_post_worklogs - Created 23 entries in one operation

Your October timesheet is now complete and matches the same project pattern you used in September!

🎯 What Happened Here:

• 3 Tool Calls: get_worklogs → get_schedule → bulk_post_worklogs

• Smart Pattern Recognition: Automatically identified PROJ-1234 from September data

• Schedule Awareness: Only filled working days, skipped weekends automatically

• Bulk Operations: Created 23 worklog entries in a single API call

• Error-Free Execution: 100% success rate with proper validation

• Natural Language: No need to remember issue keys, dates, or tool syntax

⏱️ Time Saved: This single natural language request triggered 3 MCP tool calls and replaced hours of manual timesheet entry!

🛠️ How This Was Built

This MCP server was built in just 3 hours using AI-powered development tools, demonstrating the power of modern AI-assisted coding:

Development Timeline

1. Specification Phase

   • Created the complete technical specification using GitHub Copilot with Claude Sonnet 4

   • Defined all API endpoints, data structures, and tool interfaces

   • Refined requirements through iterative conversation

2. Implementation Phase

   • Used VS Code with Claude Code to one-shot the entire implementation

   • Generated complete TypeScript codebase, tool implementations, and client logic

   • Implemented all core functionality in a single AI-assisted session

3. Refinement Phase

   • Switched back to GitHub Copilot with Claude Sonnet 4 after hitting usage limits in Claude Code

   • Fixed API payload formatting and authentication issues

   • Debugged and polished the Tempo API integration

Key Success Factors

• Clear specification first: Having a detailed spec enabled effective one-shot implementation

• AI tool synergy: Different AI tools excelled at different phases of development

• Iterative refinement: Quick feedback loops with AI assistants for debugging

This project showcases how AI-powered development can dramatically accelerate the creation of robust, production-ready tools.

✨ Features

• Get Worklogs: Retrieve worklogs for users with date range and issue filtering

• Create Worklogs: Add single worklog entries with automatic issue resolution

• Bulk Operations: Create multiple worklog entries efficiently using concurrent processing

• Delete Worklogs: Remove existing worklog entries

• Resource Access: Browse worklog data and recent issues

• Prompt Templates: Generate analysis prompts for worklog data

📦 Installation

Prerequisites

• Node.js (version 16 or higher)

• A JIRA instance with Tempo Timesheets plugin installed

• Personal Access Token for your JIRA account

NPX (Recommended)

The easiest way to use the server is with npx - no installation required:

Just configure your AI assistant to use npx @tranzact/tempo-filler-mcp-server as the command.

Development Setup (Source)

For development or customization:

1. Clone the repository:

   git clone https://github.com/TRANZACT/tempo-filler-mcp-server cd TempoFiller

2. Install dependencies and build:

   npm install && npm run build

⚙️ Configuration

The server requires environment variables for authentication and configuration:

Required Environment Variables

• TEMPO_BASE_URL: Your JIRA instance URL (e.g., https://jira.company.com)

• TEMPO_PAT: Personal Access Token for authentication

Optional Environment Variables

• TEMPO_DEFAULT_HOURS: Default hours per workday (default: 8)

Creating a Personal Access Token (PAT)

1. Log into your JIRA instance

2. Go to Profile → Personal Access Tokens

3. Click Create token

4. Give it a name (e.g., "Tempo MCP Server")

5. Set appropriate permissions (read/write access to issues and worklogs)

6. Copy the token value for use in TEMPO_PAT

🛠️ Available Tools

1. get_worklogs - Retrieve Time Logs

Retrieve worklogs for a date range with optional filtering.

Parameters:

• startDate (string): Start date in YYYY-MM-DD format

• endDate (string, optional): End date, defaults to startDate

• issueKey (string, optional): Filter by specific issue key

Example Usage:

2. post_worklog - Log Single Entry

Create a new worklog entry for a specific issue and date.

Parameters:

• issueKey (string): JIRA issue key (e.g., "PROJ-1234")

• hours (number): Hours worked (decimal, 0.1-24)

• startDate (string): Date in YYYY-MM-DD format

• endDate (string, optional): End date for multi-day entries

• billable (boolean, optional): Whether time is billable (default: true)

• description (string, optional): Work description

Example Usage:

3. bulk_post_worklogs - Create Multiple Entries

Create multiple worklog entries efficiently with concurrent processing.

Parameters:

• worklogs (array): Array of worklog objects:

  • issueKey (string): JIRA issue key

  • hours (number): Hours worked

  • date (string): Date in YYYY-MM-DD format

  • description (string, optional): Work description

• billable (boolean, optional): Whether time is billable for all entries

Example Usage:

4. delete_worklog - Remove Entry

Delete an existing worklog entry by ID.

Parameters:

• worklogId (string): Tempo worklog ID to delete

Example Usage:

5. get_schedule - Retrieve Work Schedule

Retrieve work schedule information showing working days, non-working days, and expected hours per day.

Parameters:

• startDate (string): Start date in YYYY-MM-DD format

• endDate (string, optional): End date, defaults to startDate

Example Usage:

💡 Integration Tip: Use this tool before bulk worklog operations to identify working days and avoid logging time on weekends/holidays.

🌎 Real-World Usage Examples

Based on successful implementation, here are practical scenarios:

Daily Time Logging

Schedule-First Bulk Time Filling

Monthly Time Filling (Legacy)

Schedule-Aware Planning

Time Tracking Analysis

🤖 Development

Project Structure

Build Commands

• npm run build: Compile TypeScript to JavaScript

• npm run dev: Build and run the server

• npm run typecheck: Type checking without compilation

License

ISC License - see package.json for details

Contributing

Contributions are welcome! Please follow the existing code style and ensure all tools work correctly with real Tempo API endpoints.