Linear is a streamlined issue tracking and project planning tool for modern teams, featuring fast workflows, keyboard shortcuts, and GitHub integrations
Connect your AI agents to Linear MCP in minutes. No field mapping. No code required. Adopt AI's zero-shot API discovery means your agents understand Linear's schema on first contact - and can act on it instantly.
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Linear just killed the gap between planning and execution.
Not with another feature. Not with another integration. With a protocol server that turns your issue tracker into something AI agents can actually reason about, act on, and keep synchronized across sessions.
Linear MCP is Linear's official Model Context Protocol server. It runs at https://mcp.linear.app/mcp, uses OAuth 2.1 with dynamic client registration, and exposes Linear's data and actions to any compatible AI client in a standardized, secure way. No browser automation. No custom scripts. No losing context between sessions.
The difference is architectural. Most integrations bolt AI onto tools as an afterthought. Linear MCP makes Linear native to AI workflows from the ground up. Your agent can pull issue context, create projects from conversations, update statuses, and maintain planning artifacts in your system of record instead of losing them in chat history.
This matters because the bottleneck in agentic workflows is not generation. It is memory, continuity, and keeping work artifacts synchronized with reality. Linear MCP solves that for product and engineering teams who use Linear as their planning hub.
Linear MCP is a remote MCP server hosted by Linear that follows the authenticated remote MCP specification. It is not a wrapper around Linear's API. It is not a chatbot plugin. It is infrastructure that makes Linear data and actions directly accessible to AI agents in a way that preserves context, maintains security boundaries, and works consistently across different AI clients.
The server currently supports tools for:
Linear has stated that more functionality is planned, which signals this is infrastructure meant to grow alongside agent capabilities rather than stay limited to basic CRUD operations.
The core value proposition is context continuity across sessions. Here's what that means in practice:
The breakthrough is that the AI agent treats Linear as its working memory, not as a separate system you manually sync with. That changes what kinds of workflows become possible.
A common pattern from the r/ClaudeAI community: Start a conversation about a feature, have Claude Code create a Linear project with decomposed issues, then reference those issues in implementation sessions over the next several days. The agent pulls context from Linear, implements changes, updates issue status, and adds technical notes—all without the developer manually bridging the two systems.
One developer described this as "long context for Claude Code that actually persists." The MCP connection means the agent can maintain coherent planning across multiple sessions without hitting context window limits.
Another pattern: Tell the agent "work on Linear issue ENG-234." The agent:
This is closer to how human developers actually work—Linear is the source of truth, not a side system.
Product teams use Linear MCP to turn planning conversations into structured backlogs. Instead of typing ideas into Linear manually, they discuss priorities and scope with Claude, and the agent creates properly tagged, prioritized issues with context already attached. This is especially useful for solo founders and small teams where writing detailed issue descriptions is often delayed or skipped.
Linear documents native or near-native support for:
For clients that don't support remote MCP directly, Linear recommends using mcp-remote as a compatibility bridge. This matters because MCP adoption is accelerating, and Linear's server works with any client that implements the protocol correctly—not just a single vendor's tooling.
Linear MCP supports two authentication paths:
The interactive OAuth flow built into compatible clients. For Claude Code, you open a session and run /mcp to complete authentication. This gives the agent access scoped to your Linear permissions, with proper token refresh and revocation support.
You can also pass an OAuth token or API key in an Authorization: Bearer <token> header. This path enables:
That flexibility matters because teams have different security models. Some want user-level access tied to individual logins. Others need service accounts with explicit permission boundaries.
bash
# Open Claude Code session
/mcp
# Follow OAuth flow to authenticate with Linear
# MCP connection is now active
json
{
"mcpServers": {
"linear": {
"command": "mcp-remote",
"args": ["https://mcp.linear.app/mcp"]
}
}
}
bash
# Enable remote MCP client (older setups)
codex config set experimental_use_rmcp_client true
# Add Linear MCP server
codex mcp add linear https://mcp.linear.app/mcp
The setup is straightforward for teams already using these tools. The friction is minimal because Linear handles the server infrastructure—you're not self-hosting or managing credentials manually.
Give the agent a high-level feature request, and it creates a properly scoped Linear project with sub-issues, acceptance criteria, and dependencies. This is useful for product planning where writing detailed tickets is a bottleneck.
The agent reads Linear issues before starting work, which means it has the full context of "why this matters" and "what the acceptance criteria are" without you re-explaining it every time.
Agents can update issue status as they work, which keeps Linear accurate without manual updates. This matters for teams using Linear as their source of truth for what's in progress vs completed.
Because Linear is external to the AI session, context persists. You can start a conversation Monday, continue it Wednesday, and the agent still knows what issues exist, what their status is, and what was previously discussed.
Multiple team members can use AI agents that share the same Linear workspace. Changes made by one agent are visible to others, which enables coordination without manual syncing.
Linear's docs imply the server is still expanding functionality. Not every Linear action is necessarily exposed yet. For example, advanced filtering, custom views, or roadmap manipulation may not be fully supported.
MCP behavior depends on the client implementation. The same Linear connection can feel very different in Claude Code vs Cursor vs VS Code. Some clients handle MCP tools better than others, which affects reliability.
If your AI tool doesn't support remote MCP natively, you need mcp-remote as a bridge, which adds configuration complexity. Teams using custom agents or less common tools may hit compatibility issues.
OAuth scopes control what the agent can access, but the granularity depends on Linear's permission model. If you want more restrictive access than Linear's API allows, you'll need to manage that at the integration layer.
Linear MCP is a remote server. If Linear's MCP endpoint is down or your network connection drops, the agent loses access to Linear. There's no local fallback or cached read mode.
Product Teams Already Using Linear
If Linear is already your planning hub, MCP makes AI agents dramatically more useful. The integration is native, not bolted on.
Solo Founders and Small Teams
Writing detailed Linear issues is often a bottleneck for small teams. MCP lets you brainstorm with AI and get structured backlog output without manual transcription.
Engineering Teams Using Claude Code or Cursor
These clients have strong MCP support, and Linear is a common tool in their target demographic. The workflow integration is seamless.
Agentic Coding Workflows
If your workflow involves AI agents writing code, Linear MCP keeps planning and execution synchronized. The agent knows what it's supposed to build and can update status as it progresses.
Teams That Want AI Context Continuity
If you're frustrated by AI agents "forgetting" context between sessions, Linear MCP solves that by making Linear the external memory layer.
Teams Not Using Linear
If you use Jira, Asana, or another project management tool, Linear MCP doesn't help you. (Though Jira MCP exists for similar workflows.)
Teams That Want AI Contained to Chat
If you want AI to answer questions but not take actions in your systems, MCP is infrastructure you don't need.
Organizations with Strict Agent Control Policies
If your security model doesn't allow AI agents to create, read, or update issues autonomously, Linear MCP's value proposition collapses.
Non-Technical Teams Without MCP-Compatible Tools
If your team doesn't use Claude Code, Cursor, or similar tools, setting up Linear MCP is more friction than it's worth.
Standardized Protocol
MCP is an open spec. Any client that implements it correctly can connect to Linear without custom integration work. This means as MCP adoption grows, Linear access comes for free.
OAuth Security Model
You're not managing API keys manually or writing token refresh logic. The OAuth flow handles authentication properly, with scopes, expiration, and revocation.
Maintained by Linear
Linear's team maintains the server infrastructure and keeps it updated as their API evolves. You're not debugging a stale integration when Linear ships new features.
Cross-Client Compatibility
One MCP server works with Claude Code, Cursor, VS Code, and every other MCP-compatible client. With custom integrations, you'd build separate connectors for each tool.
Non-MCP Clients
If your agent framework doesn't support MCP, you'll need custom integration code anyway. Linear MCP doesn't eliminate that need; it just makes it unnecessary for MCP-compatible clients.
Advanced Workflow Orchestration
If your automation involves complex logic, conditionals, or multi-system coordination, you may still need custom code on top of MCP. MCP provides structured access; it doesn't replace workflow engines.
Rate Limiting and Caching
Linear's MCP server is subject to their rate limits. If you need aggressive caching or offline-first behavior, a custom integration with a local cache layer might perform better.
OAuth 2.1 with PKCE
Linear uses modern OAuth with Proof Key for Code Exchange, which protects against authorization code interception attacks.
Dynamic Client Registration
Clients register themselves with Linear's authorization server, which means you're not embedding static credentials in config files.
Scope-Based Permissions
Access is scoped to what Linear's API allows for your account. The agent can't access data you couldn't access manually.
Agent Prompt Injection
If an agent with Linear access processes untrusted input (user messages, external data), a malicious actor could craft prompts that manipulate Linear data. This is a general agent security issue, not specific to Linear MCP, but the attack surface includes Linear write operations.
Overly Broad Permissions
If you authenticate an agent with your personal Linear account, it inherits all your permissions. For production workflows, consider using a service account with restricted access.
No Audit Trail Granularity
Linear MCP operations show up in Linear's activity logs, but they may appear as actions taken by the authenticated user, not clearly tagged as "agent-initiated." This can make forensics harder if something goes wrong.
Team Workspaces
Multiple team members can connect their own agents to the same Linear workspace. Changes are visible to everyone, which enables collaboration without manual syncing.
Read-Only Use Cases
If you want agents to read Linear data but not write, you can configure OAuth scopes or API keys with read-only permissions. This reduces risk for experimental workflows.
Integration with Existing Tools
Because Linear MCP is protocol-standard, it works alongside other MCP servers (GitHub, Slack, etc.) in the same agent session. This enables cross-system workflows without custom glue code.
Enterprise SSO
Linear supports SSO for user authentication, but MCP connections currently use OAuth tokens. Large enterprises with strict SSO policies may need custom integration layers.
Centralized Agent Management
There's no Linear-side dashboard for "which agents have access" or "revoke all agent tokens." You manage access through OAuth token lifecycle, which works but isn't optimized for fleet management.
Role-Based Agent Policies
You can't currently say "agents can create issues but not delete projects" at a granular level beyond what Linear's API scopes allow. If you need more restrictive agent permissions than Linear's role model supports, you'll need middleware.
1. Automated Issue Triage & Prioritization
AI agents analyze incoming Linear issues, auto-assign priority levels based on keywords and context, and route them to the right team or assignee.
2. Sprint Planning & Workload Balancing
AI agents review team capacity and backlog in Linear to recommend sprint assignments, flag overloaded team members, and suggest scope adjustments.
3. Bug Tracking & Resolution Monitoring
Automatically monitor bug issues in Linear, escalate stalled tickets, and notify engineering leads when critical bugs exceed SLA thresholds.
4. Cross-Tool Development Workflow
AI agents sync Linear issues with GitHub PRs, Slack threads, and documentation tools to keep development workflows connected and up to date.
5. Project Progress Reporting
AI agents pull cycle and project data from Linear to generate automated status reports, highlight blockers, and surface delivery risks for leadership.
Do I need my own developer credentials to use Linear MCP with Adopt AI?
No, you can get started immediately using Adopt AI's built-in Linear integration. For production use, we recommend configuring your own API credentials for greater control and security.
Can I connect Linear with other apps through Adopt AI?
Yes! Adopt AI supports multi-app workflows, so your AI agents can seamlessly move data between Linear and CRMs, spreadsheets, messaging platforms, and more.
Is Adopt AI secure?
Absolutely. Adopt AI is SOC 2 Type 2 certified and ISO/IEC 27001 compliant, and adheres to EU GDPR, CCPA, and HIPAA standards. All data is encrypted in transit and at rest, ensuring the confidentiality, integrity, and availability of your data. Learn more here.
What happens if the Linear API changes?
Adopt AI maintains and updates all integrations automatically, so your agents always work with the latest API versions, no manual maintenance required.
Do I need coding skills to set up the Linear integration?
Not at all. Adopt AI's zero-shot API discovery means your agents understand Linear's schema on first contact. Setup takes minutes with no code required.
How do I set up custom Linear MCP in Adopt AI?
For a step-by-step guide on creating and configuring your own Linear API credentials with Adopt AI, see here.
What is Linear MCP?
Linear MCP is Linear's official Model Context Protocol server that makes Linear data and actions accessible to AI agents in a standardized, secure way. It runs at https://mcp.linear.app/mcp and supports OAuth 2.1 authentication.
Which AI tools work with Linear MCP?
Claude Code, Cursor, Codex, VS Code (via mcp-remote), Jules, v0, Windsurf, and Zed all support Linear MCP. Any tool that implements the MCP specification can connect to Linear's server.
Do I need to self-host Linear MCP?
No. Linear hosts the MCP server infrastructure. You just authenticate your AI client with Linear via OAuth, and the connection works.
Is Linear MCP secure?
Linear MCP uses OAuth 2.1 with PKCE for authentication and scopes access to your Linear permissions. It's as secure as Linear's API, but remember that agents with write access can modify your Linear data based on prompts they receive.
Can multiple team members use Linear MCP with the same workspace?
Yes. Each team member authenticates their own agent with their Linear credentials, and all changes are visible in the shared workspace. This enables collaborative agent workflows.
What's the difference between Linear MCP and Linear's API?
Linear's API is what you use to build custom integrations. Linear MCP is a standardized server that exposes Linear's API to MCP-compatible AI clients without requiring custom integration code for each client.
Does Linear MCP work offline?
No. Linear MCP is a remote server. If Linear's MCP endpoint is unavailable or you lose network connectivity, agents can't access Linear.
How much does Linear MCP cost?
Linear MCP access is included with your Linear subscription. There's no separate charge for MCP functionality beyond your existing Linear plan.
Can I restrict what agents can do in Linear?
Yes, via OAuth scopes and Linear's permission model. You can authenticate agents with read-only access or service accounts with limited permissions rather than your full admin account.
What's the best way to get started with Linear MCP?
If you use Claude Code, open a session and run /mcp to authenticate with Linear via OAuth. For other clients, add Linear's MCP server URL to your client's MCP configuration and follow the authentication flow.
Should I use Linear MCP or build a custom Linear integration?
Use Linear MCP if your AI client supports MCP—it's faster, maintained by Linear, and works across multiple clients. Build a custom integration only if your client doesn't support MCP or you need capabilities beyond what Linear's MCP server exposes.
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