What can jira-cloud-mcp do?

jira issue crud operations via mcp protocol, jira project and board discovery via mcp resources, jira search and filtering via jql translation, jira issue comment and activity tracking, jira user and team resolution, jira workflow state transitions with validation, jira custom field handling and schema mapping, jira issue linking and dependency tracking, jira sprint and release planning integration

jira-cloud-mcp

MCP ServerFree

MCP server: jira-cloud-mcp

Open Source

/ 100

9 capabilities

Capabilities9 decomposed

jira issue crud operations via mcp protocol

Medium confidence

Enables LLM agents and tools to create, read, update, and delete Jira issues through the Model Context Protocol, translating MCP tool calls into Jira Cloud REST API requests with automatic authentication via OAuth2 or API tokens. Implements request/response marshaling to convert between MCP's JSON-RPC 2.0 format and Jira's REST endpoint schemas, handling field mapping and validation.

Solves for

I want my AI agent to create Jira tickets from natural language descriptions without manual formattingI need to fetch issue details and metadata programmatically to feed into downstream LLM reasoningI want to update issue status, assignee, or custom fields based on AI decisionsI need to delete or archive issues through an AI workflow without touching the Jira UI

Best for

AI teams building autonomous issue management agents

DevOps engineers automating incident response workflows with LLMs

Product teams integrating Jira with multi-step AI reasoning pipelines

Requires

Jira Cloud instance with API access enabled

OAuth2 credentials or API token with issue management scopes

MCP client implementation (Claude Desktop, custom agent framework, etc.)

Limitations

Limited to Jira Cloud API capabilities — on-premise Jira Server not supported

Bulk operations require sequential API calls (no batch endpoint), adding latency for >10 issues

Custom field handling depends on Jira instance schema — requires pre-mapping of field IDs

What makes it unique

Implements MCP protocol binding specifically for Jira Cloud, allowing LLMs to treat Jira as a native tool without custom API wrapper code — uses MCP's resource and tool discovery to expose Jira's full issue schema dynamically based on instance configuration

vs alternatives

Simpler than building custom Jira API integrations because MCP handles authentication, serialization, and tool registration; more flexible than Jira's native automation rules because it enables multi-step LLM reasoning across issues

jira project and board discovery via mcp resources

Medium confidence

Exposes Jira projects, boards, and issue hierarchies as MCP resources that LLM agents can query and traverse, implementing a resource-based discovery pattern where each project/board is addressable via a URI and returns structured metadata including board type (Scrum/Kanban), issue types, and custom fields. Uses Jira's REST API to enumerate and cache project/board listings.

Solves for

I want my agent to understand the structure of my Jira workspace before creating or updating issuesI need to list all available projects and boards so the agent can route issues to the correct destinationI want to discover custom fields and issue types supported by a specific projectI need to fetch board configuration (columns, swimlanes) to map AI decisions to valid workflow states

Best for

Multi-project organizations where agents need context-aware issue routing

Teams with complex Jira hierarchies (multiple boards per project, custom workflows)

AI systems that need to validate user input against available Jira structures

Requires

Jira Cloud instance with at least read access to projects

API token or OAuth2 credentials with project read scope

MCP client with resource discovery support

Limitations

Resource discovery is read-only — cannot modify project settings or board configuration through MCP

Caching strategy not specified — may return stale data if projects are modified outside the agent

No pagination support documented — may fail on instances with >100 projects

What makes it unique

Uses MCP's resource protocol to model Jira's hierarchical structure (workspace → projects → boards → issues) as traversable resources, enabling agents to navigate and understand Jira topology without separate discovery API calls

vs alternatives

More discoverable than raw Jira API because MCP exposes available projects/boards as first-class resources; more efficient than agents querying all endpoints because resource URIs provide direct access to specific entities

jira search and filtering via jql translation

Medium confidence

Translates natural language or structured filter parameters into Jira Query Language (JQL) and executes searches against the Jira Cloud API, returning paginated issue lists with full metadata. Implements a query builder pattern that maps common filter dimensions (assignee, status, project, date range) to JQL syntax and handles special characters/escaping.

Solves for

I want my agent to find all open issues assigned to a specific person without writing JQL manuallyI need to search for issues matching complex criteria (e.g., 'high priority bugs created in the last week')I want to retrieve issues for a specific project or epic to feed into an AI analysis pipelineI need to paginate through large result sets efficiently without loading all issues into memory

Best for

AI agents that need to query Jira as part of decision-making workflows

Teams building issue analytics or reporting systems on top of Jira

Automation systems that need to find and act on issues matching dynamic criteria

Requires

Jira Cloud instance with search API enabled

API token or OAuth2 credentials with issue search scope

Understanding of Jira issue fields and statuses in the target instance

Limitations

JQL translation is limited to common filters — complex custom JQL expressions may not be supported

Search results are limited to 50 issues per page by default; pagination requires multiple API calls

Full-text search performance depends on Jira instance indexing — may be slow on large instances

What makes it unique

Provides MCP-native search interface that abstracts JQL complexity, allowing LLMs to express queries in natural language or structured parameters rather than requiring agents to learn JQL syntax

vs alternatives

More accessible than raw JQL because it translates natural language to JQL; more powerful than simple field filters because it supports complex boolean logic and date ranges

jira issue comment and activity tracking

Medium confidence

Enables agents to read issue comments, activity history, and changelog entries through MCP tool calls, returning chronologically ordered comment threads with author metadata, timestamps, and edit history. Implements pagination for issues with many comments and supports filtering by date range or author.

Solves for

I want my agent to read the full comment thread on an issue to understand context and decision historyI need to track who made changes to an issue and when, for audit or analysis purposesI want to add comments to issues programmatically to document AI decisions or findingsI need to extract mentions or references from comments to link related issues or stakeholders

Best for

AI systems that need to understand issue context before making decisions

Audit and compliance workflows that track issue modifications

Collaborative agents that document their reasoning in issue comments

Requires

Jira Cloud instance with comment API enabled

API token or OAuth2 credentials with issue read scope

Issue key or ID to fetch comments for

Limitations

Comment editing history is limited — only current version and edit timestamp are returned

Attachment metadata is returned but file content is not accessible through MCP

Mentions and links in comments are not parsed — agents must extract them from raw text

What makes it unique

Exposes Jira's activity stream and comment history as queryable MCP resources, allowing agents to reconstruct issue context and decision rationale from the full comment thread rather than just current state

vs alternatives

More contextual than issue snapshots because it includes full comment history; more efficient than polling Jira UI because it uses the REST API with pagination support

jira user and team resolution

Medium confidence

Resolves user mentions, team names, and assignee references to Jira user objects with full metadata (email, avatar, timezone, account ID), enabling agents to map natural language names to valid Jira user IDs for assignment operations. Implements a search-based resolution pattern that queries Jira's user directory and caches results.

Solves for

I want my agent to assign issues to team members using their names instead of account IDsI need to find the correct user ID for a person mentioned in natural languageI want to validate that a user exists in Jira before attempting to assign them an issueI need to list team members for a project to enable intelligent issue routing

Best for

Multi-user teams where agents need to map names to Jira accounts

Automation systems that parse user mentions from external sources (email, Slack, etc.)

AI agents that need to validate user existence before performing operations

Requires

Jira Cloud instance with user directory enabled

API token or OAuth2 credentials with user read scope

User name or email to search for

Limitations

User search is limited by Jira's directory — external users or deactivated accounts may not be discoverable

Name matching is fuzzy and may return multiple candidates — agents must disambiguate

User metadata visibility depends on Jira permissions — some fields may be redacted

What makes it unique

Provides MCP-native user resolution that abstracts Jira's account ID complexity, allowing agents to work with human-readable names and email addresses while mapping to internal Jira identifiers

vs alternatives

More usable than raw account IDs because it supports name-based lookup; more reliable than hardcoded mappings because it queries the live Jira directory

jira workflow state transitions with validation

Medium confidence

Validates and executes issue status transitions through Jira's workflow engine, checking allowed transitions for the current issue state and enforcing required field values before moving to the next state. Implements a state machine pattern that queries Jira's workflow metadata to determine valid next states and required fields.

Solves for

I want my agent to move issues through the workflow (e.g., 'In Progress' → 'Done') with automatic validationI need to check what transitions are available for an issue before attempting to move itI want to ensure required fields are populated before transitioning to a state that requires themI need to handle workflow errors gracefully when a transition is not allowed

Best for

Automation systems that need to respect Jira workflow rules

AI agents that manage issue lifecycle without manual intervention

Teams with complex workflows that require field validation before state changes

Requires

Jira Cloud instance with workflow engine enabled

API token or OAuth2 credentials with issue edit scope

Issue key and target transition name

Limitations

Workflow validation is instance-specific — transitions vary by project and workflow

Required field detection depends on workflow configuration — may not catch all validation errors

Transition screens (custom fields required for specific transitions) are not fully exposed

What makes it unique

Implements workflow-aware state transitions that validate against Jira's workflow engine before executing, preventing invalid state changes and enforcing required field constraints defined in the workflow

vs alternatives

More robust than direct status updates because it respects workflow rules; more intelligent than blind transitions because it validates required fields and available next states

jira custom field handling and schema mapping

Medium confidence

Discovers, validates, and maps custom fields defined in a Jira instance, translating between field names and field IDs and enforcing field type constraints (select lists, date fields, number fields, etc.). Implements a schema registry pattern that caches field definitions and provides type-aware field validation.

Solves for

I want my agent to populate custom fields when creating or updating issues without knowing field IDsI need to validate that a field value matches the expected type before sending it to JiraI want to discover available options for select-list custom fields to enable intelligent field populationI need to handle custom field visibility rules (some fields only appear in certain projects)

Best for

Organizations with complex custom field configurations

AI systems that need to create or update issues with custom fields

Teams that want agents to understand and respect field constraints

Requires

Jira Cloud instance with custom fields enabled

API token or OAuth2 credentials with field read scope

Project key to discover fields for

Limitations

Custom field discovery is project-specific — field availability varies by project

Field type validation is basic — complex field types (cascading selects, linked issues) may not be fully supported

Field options for select lists are not cached — each query requires an API call

What makes it unique

Provides MCP-native custom field schema discovery and validation, allowing agents to work with field names while automatically mapping to field IDs and enforcing type constraints defined in the Jira instance

vs alternatives

More flexible than hardcoded field mappings because it discovers fields dynamically; more reliable than manual field ID lookup because it validates against the live schema

jira issue linking and dependency tracking

Medium confidence

Creates, reads, and manages issue links (relates to, blocks, duplicates, etc.) through MCP tool calls, enabling agents to establish relationships between issues and traverse dependency graphs. Implements link creation with validation of link types and bidirectional link management.

Solves for

I want my agent to link related issues together to establish dependencies or relationshipsI need to find all issues that block or depend on a specific issueI want to detect and prevent duplicate issues by linking themI need to traverse the dependency graph to understand issue impact and scope

Best for

AI systems managing complex projects with many interdependent issues

Automation workflows that need to establish issue relationships

Teams that want agents to understand and respect issue dependencies

Requires

Jira Cloud instance with issue linking enabled

API token or OAuth2 credentials with issue edit scope

Source and target issue keys

Limitations

Link types are instance-specific — available link types vary by Jira configuration

Circular dependency detection is not built-in — agents must implement their own cycle detection

Link traversal is not optimized for large graphs — deep dependency chains may require many API calls

What makes it unique

Exposes Jira's issue linking as MCP tools with bidirectional link management, allowing agents to establish and traverse issue relationships without understanding Jira's internal link ID system

vs alternatives

More discoverable than raw link API because it exposes link types as first-class concepts; more useful than read-only links because it supports link creation and deletion

jira sprint and release planning integration

Medium confidence

Integrates with Jira's sprint and release management features, enabling agents to query sprint status, move issues between sprints, and track release progress. Implements sprint-aware issue operations that respect sprint boundaries and active sprint constraints.

Solves for

I want my agent to understand which sprint an issue belongs to and move it between sprintsI need to query sprint status and progress to feed into AI-driven planning decisionsI want to automatically move issues to the next sprint based on priority or statusI need to track release progress and identify at-risk issues

Best for

Agile teams using Scrum with automated sprint management

AI systems that need to understand sprint context for issue prioritization

Release managers that want agents to track and report on release progress

Requires

Jira Cloud instance with Scrum board enabled

API token or OAuth2 credentials with sprint edit scope

Board ID and sprint ID

Limitations

Sprint operations are only available for Scrum boards — Kanban boards have no sprint concept

Active sprint constraints may prevent moving issues — agents must handle sprint closure rules

Sprint capacity and velocity data are not exposed — agents cannot predict sprint feasibility

What makes it unique

Provides MCP-native sprint management that abstracts Scrum-specific constraints, allowing agents to move issues between sprints while respecting sprint closure rules and active sprint boundaries

vs alternatives

More context-aware than simple issue updates because it understands sprint constraints; more useful than read-only sprint data because it supports sprint mutations

Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.

Related Artifactssharing capabilities

Artifacts that share capabilities with jira-cloud-mcp, ranked by overlap. Discovered automatically through the match graph.

MCP Server22

jira_just_ai

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jira issue retrieval via mcpjira project management integration via mcpjira issue creation via mcpjira issue update via mcp

4 shared capabilities

MCP Server24

jira-mcp-server

MCP server: jira-mcp-server

jira issue retrieval via mcpjira project management integration via mcpjira issue comment management via mcpjira issue creation via mcp

4 shared capabilities

MCP Server20

Jira Context MCP

** - MCP server to provide Jira Tickets information to AI coding agents like Cursor.

mcp protocol server implementation for jira integrationjira ticket context injection into ai coding agentsjira ticket search and filtering via jql queriesjira api credential management and authentication

4 shared capabilities

MCP Server33

mcp-atlassian

MCP server for Atlassian tools (Confluence, Jira)

jira issue crud operations with field-aware schema mappingjql-based issue search with faceted filtering and aggregationspace and project management with metadata and configuration queries

3 shared capabilities

MCP Server26

Jira MCP Server

** A modular and extensible MCP server designed to interact with Jira Cloud, providing tools to query boards, issues, and user data — ideal for integrating Jira with AI agents, bots, or automation systems

issue search and filtering with jql translationjira board state querying with mcp protocol bindingproject and board discovery with schema introspection

3 shared capabilities

MCP Server60

Confluence MCP Server

Search, read, and create Confluence wiki pages via MCP.

jira issue search and jql query executionjira issue creation and field management

2 shared capabilities

Best For

✓AI teams building autonomous issue management agents
✓DevOps engineers automating incident response workflows with LLMs
✓Product teams integrating Jira with multi-step AI reasoning pipelines
✓Multi-project organizations where agents need context-aware issue routing
✓Teams with complex Jira hierarchies (multiple boards per project, custom workflows)
✓AI systems that need to validate user input against available Jira structures
✓AI agents that need to query Jira as part of decision-making workflows
✓Teams building issue analytics or reporting systems on top of Jira

Known Limitations

⚠Limited to Jira Cloud API capabilities — on-premise Jira Server not supported
⚠Bulk operations require sequential API calls (no batch endpoint), adding latency for >10 issues
⚠Custom field handling depends on Jira instance schema — requires pre-mapping of field IDs
⚠No built-in retry logic for rate-limited requests (429 responses)
⚠Resource discovery is read-only — cannot modify project settings or board configuration through MCP
⚠Caching strategy not specified — may return stale data if projects are modified outside the agent

Requirements

Jira Cloud instance with API access enabledOAuth2 credentials or API token with issue management scopesMCP client implementation (Claude Desktop, custom agent framework, etc.)Network access to api.atlassian.comJira Cloud instance with at least read access to projectsAPI token or OAuth2 credentials with project read scopeMCP client with resource discovery supportJira Cloud instance with search API enabled

Input / Output

Accepts: JSON (MCP tool call parameters), text (issue summary, description, comments), structured data (field mappings, custom field values), text (project key or board ID for filtering), JSON (query parameters for filtering), text (natural language filter description), JSON (structured filter parameters: assignee, status, project, date range, priority), JQL (raw Jira Query Language for advanced searches), text (issue key or ID), JSON (pagination parameters, date range filters), text (user name, email, or display name), JSON (search parameters), text (issue key, transition name), JSON (field values required for transition), text (field name or ID), JSON (field value with type information), text (issue key, link type), JSON (link metadata), text (issue key, sprint name), JSON (sprint parameters)

Produces: JSON (issue objects with full metadata), text (issue keys, summaries, status), structured data (field values, timestamps, user references), JSON (project metadata, board configurations), structured data (issue type schemas, field definitions), JSON (paginated issue list with full metadata), text (issue summaries and keys), structured data (field values, timestamps), JSON (comment objects with author, timestamp, body), text (comment text content), structured data (activity timeline, changelog entries), JSON (user objects with account ID, email, avatar URL), text (user names and emails), structured data (user metadata), JSON (updated issue object, available transitions), text (transition status, error messages), structured data (required fields, validation errors), JSON (field schema with type, options, constraints), text (field names and descriptions), structured data (field type definitions, validation rules), JSON (link objects with type and target issue), text (issue keys and link types), structured data (dependency graph), JSON (sprint metadata, issue list, progress metrics), text (sprint status, issue counts), structured data (sprint velocity, burndown data)

UnfragileRank

Adoption5%(25% weight)

Quality18%(25% weight)

Ecosystem49%(15% weight)

Match Graph25%(30% weight)

Freshness75%(5% weight)

UnfragileRank is computed from adoption signals, documentation quality, ecosystem connectivity, match graph feedback, and freshness. No artifact can pay for a higher rank.

Type: MCP Server

9 capabilities

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MCP server: jira-cloud-mcp

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Capabilities9 decomposed

jira issue crud operations via mcp protocol

Medium confidence

Solves for

Best for

AI teams building autonomous issue management agents

DevOps engineers automating incident response workflows with LLMs

Product teams integrating Jira with multi-step AI reasoning pipelines

Requires

Jira Cloud instance with API access enabled

OAuth2 credentials or API token with issue management scopes

MCP client implementation (Claude Desktop, custom agent framework, etc.)

Limitations

Limited to Jira Cloud API capabilities — on-premise Jira Server not supported

Bulk operations require sequential API calls (no batch endpoint), adding latency for >10 issues

Custom field handling depends on Jira instance schema — requires pre-mapping of field IDs

What makes it unique

vs alternatives

jira project and board discovery via mcp resources

Medium confidence

Solves for

Best for

Multi-project organizations where agents need context-aware issue routing

Teams with complex Jira hierarchies (multiple boards per project, custom workflows)

AI systems that need to validate user input against available Jira structures

Requires

Jira Cloud instance with at least read access to projects

API token or OAuth2 credentials with project read scope

MCP client with resource discovery support

Limitations

Resource discovery is read-only — cannot modify project settings or board configuration through MCP

Caching strategy not specified — may return stale data if projects are modified outside the agent

No pagination support documented — may fail on instances with >100 projects

What makes it unique

vs alternatives

jira search and filtering via jql translation

Medium confidence

Solves for

Best for

AI agents that need to query Jira as part of decision-making workflows

Teams building issue analytics or reporting systems on top of Jira

Automation systems that need to find and act on issues matching dynamic criteria

Requires

Jira Cloud instance with search API enabled

API token or OAuth2 credentials with issue search scope

Understanding of Jira issue fields and statuses in the target instance

Limitations

JQL translation is limited to common filters — complex custom JQL expressions may not be supported

Search results are limited to 50 issues per page by default; pagination requires multiple API calls

Full-text search performance depends on Jira instance indexing — may be slow on large instances

What makes it unique

Provides MCP-native search interface that abstracts JQL complexity, allowing LLMs to express queries in natural language or structured parameters rather than requiring agents to learn JQL syntax

vs alternatives

More accessible than raw JQL because it translates natural language to JQL; more powerful than simple field filters because it supports complex boolean logic and date ranges

jira issue comment and activity tracking

Medium confidence

Solves for

Best for

AI systems that need to understand issue context before making decisions

Audit and compliance workflows that track issue modifications

Collaborative agents that document their reasoning in issue comments

Requires

Jira Cloud instance with comment API enabled

API token or OAuth2 credentials with issue read scope

Issue key or ID to fetch comments for

Limitations

Comment editing history is limited — only current version and edit timestamp are returned

Attachment metadata is returned but file content is not accessible through MCP

Mentions and links in comments are not parsed — agents must extract them from raw text

What makes it unique

vs alternatives

More contextual than issue snapshots because it includes full comment history; more efficient than polling Jira UI because it uses the REST API with pagination support

jira user and team resolution

Medium confidence

Solves for

Best for

Multi-user teams where agents need to map names to Jira accounts

Automation systems that parse user mentions from external sources (email, Slack, etc.)

AI agents that need to validate user existence before performing operations

Requires

Jira Cloud instance with user directory enabled

API token or OAuth2 credentials with user read scope

User name or email to search for

Limitations

User search is limited by Jira's directory — external users or deactivated accounts may not be discoverable

Name matching is fuzzy and may return multiple candidates — agents must disambiguate

User metadata visibility depends on Jira permissions — some fields may be redacted

What makes it unique

Provides MCP-native user resolution that abstracts Jira's account ID complexity, allowing agents to work with human-readable names and email addresses while mapping to internal Jira identifiers

vs alternatives

More usable than raw account IDs because it supports name-based lookup; more reliable than hardcoded mappings because it queries the live Jira directory

jira workflow state transitions with validation

Medium confidence

Solves for

Best for

Automation systems that need to respect Jira workflow rules

AI agents that manage issue lifecycle without manual intervention

Teams with complex workflows that require field validation before state changes

Requires

Jira Cloud instance with workflow engine enabled

API token or OAuth2 credentials with issue edit scope

Issue key and target transition name

Limitations

Workflow validation is instance-specific — transitions vary by project and workflow

Required field detection depends on workflow configuration — may not catch all validation errors

Transition screens (custom fields required for specific transitions) are not fully exposed

What makes it unique

vs alternatives

More robust than direct status updates because it respects workflow rules; more intelligent than blind transitions because it validates required fields and available next states

jira custom field handling and schema mapping

Medium confidence

Solves for

Best for

Organizations with complex custom field configurations

AI systems that need to create or update issues with custom fields

Teams that want agents to understand and respect field constraints

Requires

Jira Cloud instance with custom fields enabled

API token or OAuth2 credentials with field read scope

Project key to discover fields for

Limitations

Custom field discovery is project-specific — field availability varies by project

Field type validation is basic — complex field types (cascading selects, linked issues) may not be fully supported

Field options for select lists are not cached — each query requires an API call

What makes it unique

vs alternatives

More flexible than hardcoded field mappings because it discovers fields dynamically; more reliable than manual field ID lookup because it validates against the live schema

jira issue linking and dependency tracking

Medium confidence

Solves for

Best for

AI systems managing complex projects with many interdependent issues

Automation workflows that need to establish issue relationships

Teams that want agents to understand and respect issue dependencies

Requires

Jira Cloud instance with issue linking enabled

API token or OAuth2 credentials with issue edit scope

Source and target issue keys

Limitations

Link types are instance-specific — available link types vary by Jira configuration

Circular dependency detection is not built-in — agents must implement their own cycle detection

Link traversal is not optimized for large graphs — deep dependency chains may require many API calls

What makes it unique

Exposes Jira's issue linking as MCP tools with bidirectional link management, allowing agents to establish and traverse issue relationships without understanding Jira's internal link ID system

vs alternatives

More discoverable than raw link API because it exposes link types as first-class concepts; more useful than read-only links because it supports link creation and deletion

jira sprint and release planning integration

Medium confidence

Solves for

Best for

Agile teams using Scrum with automated sprint management

AI systems that need to understand sprint context for issue prioritization

Release managers that want agents to track and report on release progress

Requires

Jira Cloud instance with Scrum board enabled

API token or OAuth2 credentials with sprint edit scope

Board ID and sprint ID

Limitations

Sprint operations are only available for Scrum boards — Kanban boards have no sprint concept

Active sprint constraints may prevent moving issues — agents must handle sprint closure rules

Sprint capacity and velocity data are not exposed — agents cannot predict sprint feasibility

What makes it unique

Provides MCP-native sprint management that abstracts Scrum-specific constraints, allowing agents to move issues between sprints while respecting sprint closure rules and active sprint boundaries

vs alternatives

More context-aware than simple issue updates because it understands sprint constraints; more useful than read-only sprint data because it supports sprint mutations

Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.

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jira-cloud-mcp

Capabilities9 decomposed

jira issue crud operations via mcp protocol

jira project and board discovery via mcp resources

jira search and filtering via jql translation

jira issue comment and activity tracking

jira user and team resolution

jira workflow state transitions with validation

jira custom field handling and schema mapping

jira issue linking and dependency tracking

jira sprint and release planning integration

Related Artifactssharing capabilities

jira_just_ai

jira-mcp-server

Jira Context MCP

mcp-atlassian

Jira MCP Server

Confluence MCP Server

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

About

Categories

Alternatives to jira-cloud-mcp

Are you the builder of jira-cloud-mcp?

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Data Sources

jira-cloud-mcp

Capabilities9 decomposed

jira issue crud operations via mcp protocol

jira project and board discovery via mcp resources

jira search and filtering via jql translation

jira issue comment and activity tracking

jira user and team resolution

jira workflow state transitions with validation

jira custom field handling and schema mapping

jira issue linking and dependency tracking

jira sprint and release planning integration

Related Artifactssharing capabilities

jira_just_ai

jira-mcp-server

Jira Context MCP

mcp-atlassian

Jira MCP Server

Confluence MCP Server

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

About

Categories

Alternatives to jira-cloud-mcp

Are you the builder of jira-cloud-mcp?

Get the weekly brief

Data Sources