When working with GenAI coding agents like Claude Code, you often want them to interact with external systems as part of their workflow. For Jira integration, I needed a way for the agent to read and update story point estimates on tickets—a common task when breaking down work or adjusting estimates after implementation.
The Problem: No Atlassian CLI Support
I could not find a way to accomplish story point read/update operations through the Atlassian CLI. The official Atlassian CLI tools focus on other operations, and while there are third-party alternatives, I wanted something lightweight and portable that could easily be invoked by a GenAI agent.
The Solution: Custom Bash Scripts
Working with Claude Code, I created two simple bash scripts that use the Jira REST API directly:
jira-read-storypoints.sh - Read story points for one or more tickets
jira-update-storypoints.sh - Update story points for one or more tickets
Full documentation and setup instructions: dev.nathanfox.net/scripts/jira-storypoints/
Script Features
Both scripts share common characteristics that make them suitable for GenAI agent interaction:
Clear error messages: When environment variables are missing, the scripts provide helpful setup instructions
Batch operations: Process multiple tickets in a single invocation
Structured output: Consistent, parseable output format for agent consumption
Exit codes: Return appropriate exit codes (success/failure count) for programmatic handling
Reading Story Points
# Single ticket
jira-read-storypoints.sh PROJ-123
# Output: PROJ-123: 5 points - Implement user authentication
# Multiple tickets
jira-read-storypoints.sh PROJ-123 PROJ-124 PROJ-125
Updating Story Points
# Single ticket
jira-update-storypoints.sh PROJ-123 3
# Multiple tickets (pairs of ticket and points)
jira-update-storypoints.sh PROJ-123 3 PROJ-124 5 PROJ-125 8
Environment Setup
The scripts require four environment variables:
export JIRA_URL=’https://mycompany.atlassian.net’
export JIRA_EMAIL=’your-email@example.com’
export JIRA_API_TOKEN=’your_api_token_here’
export JIRA_STORY_POINTS_FIELD=’customfield_XXXXX’
Finding Your Story Points Custom Field ID
Story points in Jira are stored as a custom field, not a standard field. The field ID (e.g., customfield_10016) is unique to each Jira instance since IDs are assigned sequentially as fields are created.
Option 1: Jira Admin UI
Go to Jira Settings (gear icon) → Issues → Custom fields
Find “Story Points” or “Story point estimate” in the list
Click on it to view details
The field ID is in the URL:
.../customFields/configure?fieldId=customfield_XXXXX
Option 2: API Query
curl -s -u “your-email@example.com:$JIRA_API_TOKEN” \
“$JIRA_URL/rest/api/3/field” | jq ‘.[] | select(.name | test(”story point”; “i”)) | {name, id}’
This returns something like:
{
“name”: “Story point estimate”,
“id”: “customfield_10016”
}
Option 3: Inspect a Ticket
Query any ticket that has story points set and look for the value among custom fields:
curl -s -u “your-email@example.com:$JIRA_API_TOKEN” \
“$JIRA_URL/rest/api/3/issue/PROJ-123” | jq ‘.fields | to_entries[] | select(.key | startswith(”customfield_”))’
GenAI Agent Integration
These scripts were specifically designed to be invoked by GenAI coding agents. A typical workflow might look like:
Agent receives a task: “Estimate PROJ-456 and update Jira”
Agent reviews the ticket requirements and codebase
Agent calls
jira-update-storypoints.sh PROJ-456 5to set the estimateAgent confirms the update with
jira-read-storypoints.sh PROJ-456
The clear output format makes it easy for agents to verify operations succeeded and extract relevant information from the response.
For a complete workflow using these scripts for agent-based estimation, see Using GenAI Agents to Estimate Story Points.
Why Bash Scripts?
Bash scripts work well for GenAI agent tooling because:
Universal availability: Present on virtually all development machines
No dependencies: Beyond
curlandjq, no additional packages neededEasy invocation: Simple command-line interface that agents understand
Portable: Copy to any machine with the required environment variables
Transparent: Easy to audit what the script does