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jira_get_issue

Retrieve specific Jira issue details by providing the issue key to access project information and status.

Instructions

Get details of a specific Jira issue

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
issue_keyYesIssue key (e.g., PROJ-123)

Implementation Reference

  • The core handler function for the 'jira_get_issue' tool. It fetches the Jira issue by key using the Atlassian client, extracts relevant fields, structures them into a JiraIssue model, and returns the JSON-serialized details.
    async def _get_issue(self, arguments: dict) -> List[TextContent]:
        """Get details of a specific issue"""
        issue_key = arguments["issue_key"]
        
        issue = self.jira_client.issue(issue_key)
        fields = issue.get("fields", {})
        
        issue_details = JiraIssue(
            key=issue["key"],
            summary=fields.get("summary", ""),
            description=fields.get("description", ""),
            status=fields.get("status", {}).get("name", ""),
            assignee=fields.get("assignee", {}).get("displayName", "") if fields.get("assignee") else None,
            reporter=fields.get("reporter", {}).get("displayName", ""),
            created=fields.get("created", ""),
            updated=fields.get("updated", ""),
            priority=fields.get("priority", {}).get("name", "") if fields.get("priority") else None,
            issue_type=fields.get("issuetype", {}).get("name", ""),
            project=fields.get("project", {}).get("key", "")
        )
        
        return [TextContent(
            type="text",
            text=issue_details.model_dump_json(indent=2)
        )]
  • Tool registration in the list_tools() method, defining the name, description, and input schema for 'jira_get_issue'.
    Tool(
        name="jira_get_issue",
        description="Get details of a specific Jira issue",
        inputSchema={
            "type": "object",
            "properties": {
                "issue_key": {
                    "type": "string",
                    "description": "Issue key (e.g., PROJ-123)"
                }
            },
            "required": ["issue_key"]
        }
    ),
  • Pydantic BaseModel schema used to structure and validate the output data for the jira_get_issue tool.
    class JiraIssue(BaseModel):
        """Jira issue model"""
        key: str
        summary: str
        description: Optional[str] = None
        status: str
        assignee: Optional[str] = None
        reporter: str
        created: str
        updated: str
        priority: Optional[str] = None
        issue_type: str
        project: str
  • Dispatch logic in the call_tool() handler that routes calls to 'jira_get_issue' to the _get_issue implementation.
    elif name == "jira_get_issue":
        return await self._get_issue(arguments)
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states it 'gets details' but doesn't specify what details are returned, whether it's a read-only operation, if authentication is required, or any rate limits. This leaves significant gaps for a tool that likely interacts with an external API.

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 a single, efficient sentence that directly states the tool's purpose without any fluff. It's appropriately sized and front-loaded, making it easy to parse quickly.

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

Completeness2/5

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

Given the lack of annotations and output schema, the description is incomplete. It doesn't explain what details are returned, the response format, or any behavioral traits like error handling. For a tool that likely fetches data from Jira, this leaves too much unspecified for effective agent use.

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

Parameters3/5

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

The input schema has 100% coverage with a clear description for the 'issue_key' parameter. The description adds no additional parameter information beyond what the schema provides, such as format examples or constraints. Since schema coverage is high, the baseline score of 3 is appropriate.

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

Purpose4/5

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

The description clearly states the verb ('Get details') and resource ('specific Jira issue'), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'jira_search_issues' or 'jira_get_projects' beyond the 'specific' qualifier, which is somewhat implied but not explicit.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention that this is for retrieving a single issue by key, as opposed to 'jira_search_issues' for multiple issues or 'jira_get_projects' for project data. There are no exclusions or prerequisites stated.

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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