{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github_mcp-sooperset-mcp-atlassian","slug":"mcp-sooperset-mcp-atlassian","name":"mcp-atlassian","type":"mcp","url":"https://github.com/sooperset/mcp-atlassian","page_url":"https://unfragile.ai/mcp-sooperset-mcp-atlassian","categories":["mcp-servers"],"tags":["atlassian","confluence","jira","mcp"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github_mcp-sooperset-mcp-atlassian__cap_0","uri":"capability://tool.use.integration.jira.issue.crud.operations.with.field.aware.schema.mapping","name":"jira issue crud operations with field-aware schema mapping","description":"Exposes 45+ Jira tools that map to the Jira REST API v3, including issue creation, retrieval, updates, and deletion with automatic field schema discovery. Uses a JiraClient mixin-based architecture that adapts payloads between Cloud (*.atlassian.net) and Server/Data Center deployments, handling custom fields, issue types, and project-specific field constraints through dynamic schema introspection rather than static field mappings.","intents":["Create and update Jira issues programmatically with AI-driven field population","Query issues with JQL (Jira Query Language) and retrieve structured issue data","Manage issue transitions, status changes, and workflow state through AI agents","Handle custom fields and project-specific field requirements without manual configuration"],"best_for":["AI agents automating issue tracking workflows","Teams building Jira-integrated LLM applications","Developers migrating from REST API calls to MCP-based Jira automation"],"limitations":["Cloud and Server/Data Center APIs have subtle differences in field naming and response formats — requires runtime format adaptation that adds ~50-100ms per request","Custom field IDs are instance-specific — schemas must be discovered per Jira instance, not reusable across deployments","Bulk operations (>50 issues) require pagination and sequential API calls, no native batch endpoint support"],"requires":["Jira Cloud (*.atlassian.net) or Server/Data Center 7.0+","API token, Personal Access Token (PAT), or OAuth 2.0 3LO credentials","Network access to Jira instance"],"input_types":["structured JSON (issue key, fields, JQL query)","text (issue summary, description)","enum (issue type, status, priority)"],"output_types":["structured JSON (issue object with all fields)","array of issues (search results)","boolean (operation success)"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-sooperset-mcp-atlassian__cap_1","uri":"capability://search.retrieval.jql.based.issue.search.with.faceted.filtering.and.aggregation","name":"jql-based issue search with faceted filtering and aggregation","description":"Provides search operations that execute Jira Query Language (JQL) queries through the Jira Search API, returning paginated issue results with support for field projection, sorting, and result aggregation. Implements server-side filtering and result ordering to reduce payload size and network overhead, with built-in pagination handling for large result sets (>50 issues) that abstracts the complexity of offset/limit management from the caller.","intents":["Search for issues matching complex criteria (assignee, status, date range, custom fields) via natural language converted to JQL","Retrieve paginated search results without manually managing offset/limit parameters","Aggregate issue metrics (count by status, priority distribution) from search results","Build dynamic issue dashboards by filtering across multiple projects and issue types"],"best_for":["AI agents generating dynamic JQL queries from user intent","Dashboard and reporting tools that need faceted issue filtering","Teams building issue recommendation or triage systems"],"limitations":["JQL syntax is Jira-specific and not portable — requires knowledge of Jira field names and operators","Search results are limited to 50 issues per page by default — large result sets require multiple API calls","Complex JQL queries (deeply nested conditions) may timeout on large instances with millions of issues"],"requires":["Jira Cloud or Server/Data Center with search API enabled","Valid authentication credentials","Understanding of JQL syntax or LLM capable of generating JQL from natural language"],"input_types":["text (JQL query string)","integer (page size, offset)","array of strings (field names for projection)"],"output_types":["array of issue objects","integer (total issue count)","metadata (pagination info)"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-sooperset-mcp-atlassian__cap_10","uri":"capability://text.generation.language.comment.and.discussion.management.with.mention.and.notification.support","name":"comment and discussion management with mention and notification support","description":"Provides tools for creating, updating, and querying comments on Jira issues and Confluence pages with support for user mentions (@username) and automatic notification triggering. Uses the Jira/Confluence REST APIs to handle comment creation with mention parsing, automatic @-notification of mentioned users, and comment visibility settings (private, team, public). Comment queries return full comment history with author metadata, timestamps, and edit history, enabling AI agents to participate in issue discussions and track conversation context.","intents":["Add comments to Jira issues or Confluence pages with AI-generated insights or summaries","Mention specific users in comments to notify them of updates or decisions","Query comment history to understand discussion context and previous decisions","Update or delete comments to correct information or remove outdated content"],"best_for":["AI agents that need to participate in issue discussions and provide feedback","Teams using comments for decision tracking and audit trails","Notification systems that need to mention users based on issue context"],"limitations":["Mention parsing is manual — requires knowledge of Jira/Confluence user handles, no fuzzy matching or autocomplete","Comment visibility is per-comment — cannot bulk-update visibility across multiple comments","Comment edit history is not queryable — only current comment content is returned, previous versions are not accessible","Notifications are triggered automatically but not queryable — cannot determine if a mention was actually delivered"],"requires":["Jira issue key or Confluence page ID","Valid authentication credentials with comment creation permissions","User handles for mentions (if using @-notation)"],"input_types":["text (comment body with optional @mentions)","string (issue key or page ID)","enum (visibility: private, team, public)"],"output_types":["structured JSON (comment object with metadata)","array of comments (issue/page discussion history)","boolean (comment creation success)"],"categories":["text-generation-language","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-sooperset-mcp-atlassian__cap_11","uri":"capability://tool.use.integration.attachment.upload.and.retrieval.with.content.type.detection","name":"attachment upload and retrieval with content type detection","description":"Provides tools for uploading files to Jira issues and Confluence pages, with automatic content type detection and file size validation. Supports both binary files (images, PDFs, archives) and text files, with automatic MIME type detection from file extension or content inspection. Attachment retrieval returns download URLs and metadata (filename, size, upload date, uploader), enabling AI agents to attach generated artifacts (reports, images, documents) to issues without manual file handling.","intents":["Attach generated reports, images, or documents to Jira issues or Confluence pages","Retrieve attachment metadata and download URLs for issue artifacts","Validate file types and sizes before upload to prevent invalid attachments","Manage attachment lifecycle (upload, retrieve, delete)"],"best_for":["AI agents that generate artifacts (reports, images, diagrams) and need to attach them to issues","Documentation systems that need to embed generated content in Confluence pages","Automation workflows that need to handle file attachments"],"limitations":["File size limits are instance-specific — typically 20MB for Jira Cloud, 250MB for Server/DC, requires pre-upload validation","Content type detection is based on file extension — binary files with wrong extensions may be rejected","Attachment URLs are temporary and may expire — cannot rely on long-term URL stability","Bulk attachment operations are not supported — must upload files individually"],"requires":["Jira issue key or Confluence page ID","File to upload (binary or text)","Valid authentication credentials with attachment permissions"],"input_types":["binary file (image, PDF, archive, etc.)","text file (document, code, etc.)","string (filename, optional MIME type)"],"output_types":["structured JSON (attachment metadata)","string (download URL)","array of attachments (issue/page attachments)"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-sooperset-mcp-atlassian__cap_12","uri":"capability://tool.use.integration.user.and.permission.management.with.role.based.access.control","name":"user and permission management with role-based access control","description":"Exposes tools for querying user information, managing user assignments to issues, and checking permissions for specific operations. Implements role-based access control (RBAC) queries that determine if a user has permission to perform an action (edit issue, create page, etc.) without attempting the operation. User queries return user metadata (name, email, avatar, active status) and can filter by project or issue context, enabling AI agents to assign issues to appropriate team members and validate permissions before attempting operations.","intents":["Query user information and availability for issue assignment","Assign issues to team members based on expertise or capacity","Check permissions before attempting operations to provide better error messages","Find users by name or email for mention and notification purposes"],"best_for":["AI agents that need to assign issues to team members","Permission validation systems that check access before operations","User discovery and team management tools"],"limitations":["User queries are instance-specific — user lists cannot be shared across Jira instances","Permission checks are read-only — cannot modify permissions through the API","User availability (busy, on vacation) is not queryable — requires external calendar integration","User search is limited to exact name/email matches — no fuzzy matching or autocomplete"],"requires":["Jira Cloud or Server/Data Center with user management enabled","Valid authentication credentials with user query permissions"],"input_types":["string (username, email, or display name)","string (issue key or project key, for context-specific queries)","enum (permission type: edit, create, delete, etc.)"],"output_types":["structured JSON (user object with metadata)","array of users (search results)","boolean (permission check result)"],"categories":["tool-use-integration","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-sooperset-mcp-atlassian__cap_13","uri":"capability://tool.use.integration.space.and.project.management.with.metadata.and.configuration.queries","name":"space and project management with metadata and configuration queries","description":"Provides tools for querying Confluence spaces and Jira projects, including space/project metadata (name, key, description, avatar), configuration (permissions, issue types, custom fields), and member lists. Implements hierarchical space navigation (space → pages → children) and project-specific field discovery (custom fields, issue types, workflows), enabling AI agents to understand the structure of Confluence/Jira instances and adapt operations based on project-specific constraints.","intents":["Discover available Confluence spaces and Jira projects for content organization","Query project-specific field configurations to understand custom fields and issue types","Retrieve space/project metadata for dashboard or reporting applications","Navigate space hierarchies to find parent spaces or related content"],"best_for":["AI agents that need to understand Jira/Confluence instance structure","Dashboard and reporting tools that aggregate data across projects/spaces","Content discovery and navigation systems"],"limitations":["Space/project queries are read-only — cannot create or modify spaces/projects through the API","Custom field configurations are instance-specific — cannot be shared across instances","Workflow queries return only workflow names, not detailed state transitions or conditions","Permission queries are limited to user-specific checks — cannot enumerate all permissions for a space/project"],"requires":["Jira Cloud or Server/Data Center (for projects)","Confluence Cloud or Server/Data Center (for spaces)","Valid authentication credentials"],"input_types":["string (space key or project key)","enum (query type: metadata, fields, members, etc.)"],"output_types":["structured JSON (space/project metadata)","array of custom fields (with types and constraints)","array of issue types (with workflows)","array of members (with roles)"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-sooperset-mcp-atlassian__cap_14","uri":"capability://tool.use.integration.dependency.injection.and.context.management.for.multi.tenant.deployments","name":"dependency injection and context management for multi-tenant deployments","description":"Implements a dependency injection (DI) system using Python context managers and async context managers to provide JiraClient and ConfluenceClient instances to tool handlers, with per-request context isolation for multi-tenant deployments. Uses MainAppContext to store shared configuration (base URLs, authentication method) and per-request context to store user-specific credentials (from HTTP headers), enabling multiple users to authenticate with different credentials through the same server instance without credential leakage or cross-contamination.","intents":["Support multi-tenant deployments where each request uses different credentials","Isolate per-request context to prevent credential leakage between concurrent requests","Simplify tool implementation by injecting pre-configured clients instead of passing credentials","Enable credential rotation and refresh without restarting the server"],"best_for":["Multi-tenant SaaS platforms that need per-user authentication","Shared MCP service deployments with multiple concurrent users","Teams building extensible MCP servers with complex dependency graphs"],"limitations":["Context isolation is per-request — long-running operations (streaming, webhooks) may lose context if not properly managed","Dependency injection adds complexity to tool implementation — requires understanding of async context managers","No built-in credential caching — each request must provide credentials, no session-based authentication","Context cleanup is manual — requires careful use of async context managers to prevent resource leaks"],"requires":["Python 3.9+ with async/await support","FastMCP framework with async context manager support","Understanding of Python dependency injection patterns"],"input_types":["HTTP headers (for per-request authentication)","configuration objects (for shared context)"],"output_types":["JiraClient instance (injected into tool handlers)","ConfluenceClient instance (injected into tool handlers)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-sooperset-mcp-atlassian__cap_2","uri":"capability://tool.use.integration.confluence.page.crud.with.hierarchical.space.and.content.structure.navigation","name":"confluence page crud with hierarchical space and content structure navigation","description":"Exposes 27+ Confluence tools for creating, reading, updating, and deleting pages within hierarchical space structures, with support for parent-child page relationships and content versioning. Uses the Confluence REST API v2 (Cloud) or v1 (Server/DC) with automatic content format adaptation between storage format (XHTML-like) and view format (rendered HTML), enabling AI agents to work with human-readable content while preserving Jira markup and embedded resources.","intents":["Create and organize documentation pages within Confluence spaces with parent-child hierarchies","Update page content with AI-generated or modified text while preserving formatting and embedded resources","Retrieve page content in human-readable format for analysis or summarization by LLMs","Navigate space structures and discover related pages through parent/child relationships"],"best_for":["AI agents generating or updating technical documentation","Teams building knowledge base management systems","Developers automating wiki-style content organization"],"limitations":["Content format conversion (storage ↔ view) adds ~100-150ms per page operation due to server-side rendering","Embedded resources (images, attachments) are stored separately — page updates don't automatically sync resource references","Cloud and Server/Data Center have different API versions (v2 vs v1) with incompatible response schemas, requiring runtime format adaptation"],"requires":["Confluence Cloud (*.atlassian.net) or Server/Data Center 6.0+","API token or OAuth 2.0 credentials with content:read and content:write scopes","Space key (identifier) for target space"],"input_types":["text (page title, body content)","integer (parent page ID for hierarchy)","enum (content format: storage or view)","string (space key)"],"output_types":["structured JSON (page object with metadata)","text (rendered HTML or storage format content)","array of pages (space children or search results)"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-sooperset-mcp-atlassian__cap_3","uri":"capability://tool.use.integration.multi.transport.mcp.server.with.stdio.sse.and.http.streaming.support","name":"multi-transport mcp server with stdio, sse, and http streaming support","description":"Implements the Model Context Protocol (MCP) server specification across three transport modes: stdio (for IDE integration), Server-Sent Events (SSE, for browser-based clients), and streamable-http (for service deployments). Uses FastMCP framework with async/await patterns and dependency injection to mount separate Jira and Confluence MCP instances on a shared AtlassianMCP parent server, enabling a single deployment to serve multiple AI platforms (Claude, Cursor, custom LLM clients) through their preferred transport mechanism.","intents":["Integrate Atlassian tools into IDE-based AI assistants (Claude in Cursor) via stdio transport","Deploy Atlassian MCP as a service accessible to web-based LLM clients via SSE or HTTP streaming","Support multiple concurrent AI clients connecting to the same Jira/Confluence instance","Simplify deployment by choosing transport mode at runtime without code changes"],"best_for":["Teams deploying MCP servers to multiple AI platforms (Claude, Cursor, custom agents)","Organizations needing both IDE integration and service-based MCP deployments","Developers building multi-tenant MCP services with per-request authentication"],"limitations":["Stdio transport is synchronous and single-threaded — only one IDE client per server instance, requires separate processes for concurrent IDE connections","SSE transport has no built-in reconnection logic — client disconnects require manual reconnection handling","HTTP streaming transport requires external load balancer for horizontal scaling — no built-in clustering or session affinity"],"requires":["Python 3.9+","FastMCP framework (included in dependencies)","For stdio: IDE with MCP client support (Cursor, VS Code with extension)","For SSE/HTTP: HTTP server infrastructure (can run standalone or behind reverse proxy)"],"input_types":["MCP protocol messages (tool calls, resource requests)","HTTP headers (for multi-tenant authentication)","JSON (tool arguments)"],"output_types":["MCP protocol responses (tool results, resources)","HTTP status codes and JSON responses","streaming text (for long-running operations)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-sooperset-mcp-atlassian__cap_4","uri":"capability://safety.moderation.flexible.multi.method.authentication.with.oauth.2.0.api.tokens.and.pat.support","name":"flexible multi-method authentication with oauth 2.0, api tokens, and pat support","description":"Supports four authentication methods (API tokens, Personal Access Tokens, OAuth 2.0 3LO, bring-your-own-token) with environment variable precedence and per-request HTTP header overrides for multi-tenant deployments. Uses a configuration cascade that checks environment variables first, then HTTP headers (for service deployments), enabling both single-tenant (env-based) and multi-tenant (header-based) authentication patterns without code changes. OAuth 2.0 3LO flow is handled through a callback-based mechanism that exchanges authorization codes for access tokens, with automatic token refresh.","intents":["Authenticate to Jira/Confluence using API tokens for single-tenant deployments","Support OAuth 2.0 3LO for user-delegated access in multi-tenant SaaS applications","Enable per-request authentication via HTTP headers for shared MCP service deployments","Manage token lifecycle (refresh, expiration) without manual intervention"],"best_for":["Single-tenant deployments using API tokens or PAT","Multi-tenant SaaS platforms requiring per-user authentication","Organizations with OAuth 2.0 SSO requirements","Teams needing to rotate credentials without redeploying"],"limitations":["OAuth 2.0 3LO requires a callback URL and browser-based authorization flow — not suitable for headless/CLI-only deployments","Token refresh logic is in-memory — tokens are lost on server restart, requires external token store for production","HTTP header authentication (multi-tenant) requires HTTPS and careful header validation to prevent credential leakage","API token and PAT methods store credentials in environment variables — vulnerable to process inspection or log leakage if not carefully managed"],"requires":["For API tokens: Jira/Confluence API token (generated in account settings)","For PAT: Personal Access Token (Jira Cloud only)","For OAuth 2.0: OAuth app registered in Atlassian Developer Console with client ID and secret","For multi-tenant: HTTPS endpoint and secure header transmission"],"input_types":["environment variables (JIRA_TOKEN, CONFLUENCE_TOKEN, etc.)","HTTP headers (X-Jira-Token, X-Confluence-Token for multi-tenant)","OAuth authorization code (from browser redirect)"],"output_types":["authenticated HTTP requests to Jira/Confluence APIs","access tokens (OAuth 2.0)","boolean (authentication success/failure)"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-sooperset-mcp-atlassian__cap_5","uri":"capability://tool.use.integration.agile.board.and.sprint.management.with.backlog.and.workflow.state.tracking","name":"agile board and sprint management with backlog and workflow state tracking","description":"Exposes tools for managing Jira Agile boards, sprints, and backlogs, including sprint creation, issue assignment to sprints, board state queries, and workflow transitions. Implements board-specific queries that retrieve sprint metadata (start date, end date, goal), issue assignments, and board configuration (columns, swimlanes) through the Jira Agile API, with automatic handling of Scrum vs Kanban board differences and sprint state transitions (planning → active → closed).","intents":["Query current sprint status and retrieve issues assigned to active sprints","Move issues between sprints or backlog programmatically","Transition issues through workflow states (To Do → In Progress → Done) based on AI-driven decisions","Retrieve board configuration and sprint metadata for dashboard or reporting applications"],"best_for":["AI agents managing sprint planning and issue triage","Teams building agile project dashboards","Automation workflows that need to track sprint progress and issue velocity"],"limitations":["Agile API is Jira-specific and not available in Confluence — requires separate Jira instance","Sprint state transitions are unidirectional (planning → active → closed) — cannot reopen closed sprints through API","Board configuration (columns, swimlanes) is read-only — custom board layouts require manual Jira UI configuration","Kanban boards have no sprint concept — sprint-related operations fail silently or return empty results"],"requires":["Jira Cloud or Server/Data Center with Agile (Scrum or Kanban) enabled","Board ID (can be discovered through board list API)","Valid authentication credentials"],"input_types":["integer (board ID, sprint ID, issue key)","string (sprint name, goal)","enum (sprint state: planning, active, closed)"],"output_types":["structured JSON (sprint object with metadata)","array of issues (sprint backlog)","board configuration (columns, swimlanes)"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-sooperset-mcp-atlassian__cap_6","uri":"capability://data.processing.analysis.content.transformation.and.format.normalization.storage.view.markdown","name":"content transformation and format normalization (storage ↔ view ↔ markdown)","description":"Implements bidirectional content format conversion between Confluence storage format (XHTML-like), view format (rendered HTML), and markdown for AI consumption. Uses server-side rendering APIs (Confluence Content Transformation API) to convert between formats, preserving embedded resources, links, and markup while adapting content for different consumption patterns (AI-readable markdown vs browser-rendered HTML vs storage format for updates).","intents":["Convert Confluence page content to markdown for LLM analysis and summarization","Transform AI-generated markdown back to Confluence storage format for page updates","Preserve embedded resources (images, attachments, macros) during format conversion","Normalize content across Cloud and Server/Data Center deployments with different format support"],"best_for":["AI agents that need to read and write Confluence content in markdown format","Documentation generation systems that consume Confluence pages","Content migration tools that need format normalization"],"limitations":["Confluence macros (embedded widgets, code blocks) may not convert cleanly to markdown — complex macros are often lost or rendered as plain text","Server-side rendering adds 100-150ms latency per conversion operation","Markdown → storage format conversion is lossy for Confluence-specific features (macros, permissions, labels) — requires manual post-processing","Cloud and Server/Data Center have different transformation API endpoints and response formats"],"requires":["Confluence Cloud or Server/Data Center with content transformation API enabled","Valid authentication credentials","Source content in one of the supported formats (storage, view, markdown)"],"input_types":["text (content in storage, view, or markdown format)","enum (source format: storage, view, markdown)","enum (target format: storage, view, markdown)"],"output_types":["text (converted content in target format)","metadata (conversion warnings, unsupported features)"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-sooperset-mcp-atlassian__cap_7","uri":"capability://tool.use.integration.tool.registration.and.discovery.with.schema.based.function.calling","name":"tool registration and discovery with schema-based function calling","description":"Implements MCP tool registration through FastMCP's decorator-based system, where each Jira/Confluence operation is registered as a callable tool with JSON schema describing inputs, outputs, and constraints. Uses dependency injection to provide JiraClient and ConfluenceClient instances to tool handlers, with automatic schema generation from Python type hints and docstrings. Tool discovery is handled through the MCP protocol's list_tools endpoint, enabling AI platforms to discover available operations and their signatures at runtime without hardcoded tool lists.","intents":["Enable AI platforms to discover available Jira/Confluence operations dynamically","Provide structured tool schemas (JSON schema) for AI platforms to validate inputs before calling","Support type-safe tool invocation with automatic input validation and error handling","Simplify tool implementation by using Python decorators and type hints instead of manual schema definition"],"best_for":["AI platforms (Claude, Cursor, custom agents) that support MCP tool calling","Teams building extensible MCP servers with dynamic tool registration","Developers who prefer Python decorators and type hints over manual schema definition"],"limitations":["Tool schemas are generated from Python type hints — complex types (nested objects, unions) may not translate cleanly to JSON schema","Tool discovery is static at server startup — adding/removing tools requires server restart","Schema generation from docstrings is fragile — requires careful formatting and may miss edge cases","No built-in tool versioning — breaking changes to tool signatures require client-side updates"],"requires":["FastMCP framework (included in dependencies)","Python 3.9+ with type hints support","MCP client that supports tool calling (Claude, Cursor, custom)"],"input_types":["Python function with @tool decorator","type hints (for input/output schema generation)","docstring (for tool description)"],"output_types":["JSON schema (tool signature)","tool list (from MCP list_tools endpoint)","tool result (from MCP call_tool endpoint)"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-sooperset-mcp-atlassian__cap_8","uri":"capability://tool.use.integration.worklog.and.time.tracking.with.automatic.duration.calculation","name":"worklog and time tracking with automatic duration calculation","description":"Provides tools for creating, updating, and querying issue worklogs (time spent on tasks) with automatic duration calculation from start/end times or explicit duration values. Supports worklog visibility settings (private, team, public) and automatic timestamp handling (created/updated dates), enabling AI agents to track time spent on issues without manual date/time formatting. Worklogs are stored per-issue and can be queried individually or aggregated across issues for reporting.","intents":["Log time spent on Jira issues with automatic duration calculation","Query worklog history for time tracking and reporting","Update worklog entries with corrected durations or visibility settings","Aggregate worklogs across issues for team capacity planning and velocity tracking"],"best_for":["Teams using Jira for time tracking and capacity planning","AI agents that need to log time on behalf of users","Reporting systems that aggregate worklog data for metrics"],"limitations":["Worklog visibility is per-entry — cannot bulk-update visibility across multiple worklogs","Duration calculation from start/end times requires timezone awareness — may produce incorrect durations if timezone is not specified","Worklog queries are per-issue — no cross-issue worklog search or aggregation API, requires client-side aggregation","Worklog creation requires issue-level permissions — cannot create worklogs for issues the user cannot edit"],"requires":["Jira Cloud or Server/Data Center with time tracking enabled","Issue key (to associate worklog with issue)","Valid authentication credentials with issue edit permissions"],"input_types":["string (issue key)","integer (duration in seconds or minutes)","datetime (start time, optional)","enum (visibility: private, team, public)"],"output_types":["structured JSON (worklog object with metadata)","array of worklogs (per-issue history)","integer (total duration across worklogs)"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-sooperset-mcp-atlassian__cap_9","uri":"capability://tool.use.integration.issue.linking.and.relationship.management.with.link.type.validation","name":"issue linking and relationship management with link type validation","description":"Exposes tools for creating, updating, and querying issue links (relationships between issues) with automatic link type validation and bidirectional relationship handling. Supports standard link types (blocks, relates to, duplicates, etc.) and custom link types defined in the Jira instance, with automatic enforcement of link type constraints (e.g., preventing circular dependencies in 'blocks' relationships). 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