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ccampora

Jira Data Center MCP Server

by ccampora

Create Jira Backlog Items From Requirements

create_jira_story_from_requirements

Convert workshop notes or Fit/Gap analysis into Jira stories, tasks, and bugs, then create them in your project. Supports pre-structured items and dry-run preview.

Instructions

Turns workshop notes or Fit/Gap analysis text into properly structured Jira Stories, Tasks, and Bugs, then creates them in the given project. If items is provided, those pre-structured items are created as-is (recommended when the calling agent has already analyzed the notes). Otherwise, notes is parsed heuristically: lines tagged 'Story:'/'Task:'/'Bug:', 'As a ... I want ... so that ...' phrasing (-> Story), and bullet/numbered lines (-> Task) are detected automatically. Use dryRun to preview extracted items before creating anything in Jira.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
itemsNoOptional pre-structured backlog items; overrides automatic parsing of `notes`
notesNoRaw workshop notes / Fit-Gap analysis text to auto-parse into backlog items
dryRunNoIf true, only return the parsed/would-create items without calling Jira
projectKeyYesTarget project key, e.g. 'ABC'
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden and does well by disclosing the creation behavior, heuristic parsing rules (tagged lines, 'As a...' phrasing, bullets), and dryRun safety. It does not explicitly mention permissions or reversibility, but the mutation and preview behavior are well explained.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is appropriately sized at about three sentences, front-loaded with the primary purpose, and every sentence earns its place by explaining modes, parsing rules, and dryRun. It is concise yet thorough.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity and lack of output schema, the description covers the main workflow, parsing heuristics, and preview capability. It does not explicitly state the return value (e.g., created issues vs. parsed items), but the overall operation is well enough specified for an agent to use it effectively.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so baseline is 3. The description adds value by explaining the relationship between `items` and `notes` (items override parsing and are recommended for pre-analyzed content) and clarifies that `dryRun` previews without creating. This adds semantic meaning beyond the schema's individual field descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a specific verb 'Turns... into... and creates' with a clear resource (Jira Stories, Tasks, Bugs) and target project. It clearly distinguishes itself from sibling tools like create_issue by focusing on parsing requirements text into structured backlog items.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives clear usage context: use `items` when pre-structured, use `notes` for automatic parsing, and use `dryRun` to preview. While it does not explicitly contrast with alternatives like `create_issue`, the mode-selection guidance is strong and practical.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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