Developer Tools
Jira
Page Title
Scrum masters and project managers live inside Jira's customizable workflows, sprints, and backlogs. Adjacent teams need that same data to make decisions, but Jira's permission schemes, custom field sprawl, and JQL dependency create more noise than insight.
Why it matters
Capabilities that matter most for {Name}
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Example AI Use Cases for {Name}
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Template Header
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Adds a comment to a ticket in Zendesk
Returns a filtered list of agents from Zendesk
Return a filtered list of customers from Zendesk
Adds a new ticket in Zendesk
Returns a filtered list of agents from Zendesk
Return a filtered list of customers from Zendesk
Direct connection
{Name} and out of-the-box MCP
Atlassian provides the Rovo MCP Server, giving AI clients direct read and write access to Jira Cloud, Confluence, and Compass from a single endpoint where a single JQL response returns ~10,000 tokens of nested JSON for 10 backlog items before a single comment is fetched.
Example
What's in the backlog for the Payments team and what should we prioritize for next sprint?
Jira MCP Server
Consumes ~10,000 tokens of JSON for 10 items; each issue includes nested issuetype, project with avatar URLs, full statusCategory hierarchy, and complete description text. Comments require a separate call per issue (10 more round-trips).
PopdockAI
Key, Summary, Status, Priority, Story Points, and latest Comment per item; joined and summarized in a single call with only configured fields returned.
Outcome
1 call instead of 10+ using up to 95% fewer tokens per sprint planning query
No comparison data available. Bind the "Table Data" prop to a CMS Plain Text field containing JSON.
Debug — typeof tableData: string | raw (0 chars): ""
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