MCP Linear Server
Community
79·Strong
Overall Trust Score
Community-maintained MCP server for Linear project management integration. Enables AI models to create, read, update issues, manage projects, track cycles, and coordinate teams. Essential for AI-powered software development workflows, sprint planning, and issue tracking automation.
project-management
issue-tracking
mcp
model-context-protocol
Trust Vector
Performance & Reliability
86
issue operation reliability92
issue operation reliability
92
Methodology
Operation success rate testing
Evidence
Confidence: highLast verified: 2025-11-09
search accuracy88
search accuracy
88
Methodology
Search quality testing
Evidence
Confidence: highLast verified: 2025-11-09
real time sync85
real time sync
85
Methodology
Sync reliability testing
Evidence
Confidence: highLast verified: 2025-11-09
query performance82
query performance
82
Methodology
Performance benchmarking
Evidence
Confidence: mediumLast verified: 2025-11-09
error recovery85
error recovery
85
Methodology
Error handling testing
Evidence
Confidence: mediumLast verified: 2025-11-09
Security
73
authentication security82
authentication security
82
Methodology
Authentication mechanism review
Evidence
Confidence: highLast verified: 2025-11-09
token exposure risk68
token exposure risk
68
Methodology
Token security analysis
Evidence
Confidence: highLast verified: 2025-11-09
issue modification risk70
issue modification risk
70
Methodology
Operation authorization testing
Evidence
Confidence: highLast verified: 2025-11-09
team access control78
team access control
78
Methodology
Access control testing
Evidence
Confidence: mediumLast verified: 2025-11-09
issue deletion risk72
issue deletion risk
72
Methodology
Deletion risk assessment
Evidence
Confidence: mediumLast verified: 2025-11-09
audit logging80
audit logging
80
Methodology
Audit logging review
Evidence
Confidence: highLast verified: 2025-11-09
Privacy & Compliance
69
issue data exposure66
issue data exposure
66
Methodology
Data flow analysis
Evidence
Confidence: highLast verified: 2025-11-09
team member privacy68
team member privacy
68
Methodology
User privacy assessment
Evidence
Confidence: mediumLast verified: 2025-11-09
project structure exposure72
project structure exposure
72
Methodology
Structure privacy assessment
Evidence
Confidence: mediumLast verified: 2025-11-09
third party data sharing70
third party data sharing
70
Methodology
Data sharing analysis
Evidence
LLM Provider Policies
Issue and project data shared with LLM provider per their privacy policy
Date: 2025-11-16
Confidence: highLast verified: 2025-11-09
attachment privacy70
attachment privacy
70
Methodology
Attachment privacy assessment
Evidence
Confidence: mediumLast verified: 2025-11-09
Trust & Transparency
81
documentation quality78
documentation quality
78
Methodology
Documentation completeness review
Evidence
Confidence: mediumLast verified: 2025-11-09
operation visibility85
operation visibility
85
Methodology
Logging and traceability assessment
Evidence
Confidence: highLast verified: 2025-11-09
open source transparency88
open source transparency
88
Methodology
Source code review
Evidence
Confidence: highLast verified: 2025-11-09
api coverage clarity73
api coverage clarity
73
Methodology
API documentation review
Evidence
Confidence: mediumLast verified: 2025-11-09
Operational Excellence
84
ease of setup85
ease of setup
85
Methodology
Setup complexity assessment
Evidence
Confidence: highLast verified: 2025-11-09
api performance85
api performance
85
Methodology
Performance benchmarking
Evidence
Confidence: highLast verified: 2025-11-09
reliability88
reliability
88
Methodology
Reliability analysis
Evidence
Confidence: highLast verified: 2025-11-09
operation coverage82
operation coverage
82
Methodology
Feature coverage assessment
Evidence
Confidence: highLast verified: 2025-11-09
community support75
community support
75
Methodology
Community support assessment
Evidence
Confidence: mediumLast verified: 2025-11-09
✨ Strengths
- •Comprehensive Linear integration (issues, projects, cycles, teams)
- •Built on fast and reliable GraphQL API
- •Excellent for agile development and sprint planning workflows
- •Comprehensive activity logging and audit trail
- •Open source community implementation
- •Real-time updates via webhooks and subscriptions
⚠️ Limitations
- •Issue titles, descriptions, and comments exposed to LLM provider
- •AI can create, modify, and close issues within permissions
- •Team member information and activity data accessible
- •Project roadmaps and sprint planning data may be revealed
- •Issue attachments and links accessible
- •Requires careful team permission configuration
📊 Metadata
license: MIT
supported platforms:
0: All platforms with Node.js/Python
programming languages:
0: TypeScript
1: Python
mcp version: 1.0
github repo: https://github.com/modelcontextprotocol/servers
api dependency: Linear GraphQL API
authentication: Linear API Token or OAuth
first release: 2024-11
maintained by: Community
Use Case Ratings
code generation
88
Excellent for issue-driven development and sprint planning automation
customer support
82
Good for support ticket tracking and customer issue management
content creation
70
Moderate applicability for content project tracking
data analysis
85
Good for sprint analytics, velocity tracking, and team metrics
research assistant
75
Useful for research task tracking and project organization
legal compliance
62
Risk of exposing confidential project information
healthcare
58
Risk of exposing healthcare project details
financial analysis
65
Moderate risk for financial project management
education
80
Good for managing educational project assignments
creative writing
72
Useful for tracking writing projects and story tasks