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Connect Linear's issue tracking, cycles, and project data to AI agents. Query sprint health, track velocity, and surface blockers without leaving your workflow.
Ask about cycle progress, issue status, team velocity, or roadmap health. Your AI agent pulls real-time data from Linear—issues, projects, cycles, teams, labels, estimates, and SLAs.
Your AI agent reads harmonized data across 500+ platforms. "Cost" in Google Ads and "spend" in Meta Ads resolve to the same field automatically.
Triage issues, update statuses, adjust estimates, and modify labels. Bulk operations that would take 20 clicks happen in one prompt.
250+ governance rules enforce naming conventions, budget limits, and KPI thresholds. SOC 2 Type II certified.
Track cycle health, velocity trends, and team capacity. Get notified when scope creeps, SLAs slip, or blockers pile up.
Automated weekly reports, anomaly flagging, and budget alerts — all from a single conversation. No more morning check-ins across 5 dashboards.
Triage issues, update statuses, adjust estimates, and modify labels. Bulk operations that would take 20 clicks happen in one prompt.
Every phase runs through the same MCP connection. One protocol, all platforms, full governance. No switching between tools.
You manage multiple teams using Linear. Each has different estimation scales, cycle lengths, and project types. Leadership asks: 'Which team is most efficient?' You spend an hour exporting data, normalizing estimates, and building comparison tables. By the time you have an answer, it's outdated.
Ask your AI agent to compare team velocity normalized by cycle length and estimate scale. It pulls issue completion rates, average cycle time, and scope creep metrics across teams. Updated every time you ask.
Cycles start clean. Two weeks in, you're drowning. Issues appear. Priorities shift. Suddenly you're at 130% capacity and no one noticed when it happened. You need to know the moment scope starts creeping, not during retro.
Set up monitoring that tracks issue creation after cycle start. Your AI agent flags when new issues exceed a threshold, identifies who's adding them, and shows impact on team capacity. Catch scope creep in real-time.
Friday at 4pm. Leadership wants a roadmap update. You need status across 15 projects, 6 teams, and 200+ issues. You're clicking through Linear views, copying data into slides, and praying nothing changes before Monday's meeting.
Ask for a roadmap summary. Your AI agent aggregates project progress, surfaces at-risk initiatives, and identifies blockers across all teams. Generate the same rollup in 30 seconds whenever you need it.
Same MCP connection, different workflows for every team member. Agency CEOs get portfolio health. Media Strategists get campaign QA. Analysts get cross-platform reports. Account Managers get auto-generated QBR decks. Creative Directors get performance-based briefs.
Each role asks in natural language. The MCP server handles the complexity — rate limits, auth, schema normalization, governance — behind the scenes.
Issues, projects, cycles, teams, labels, estimates, priorities, assignees, statuses, comments, SLA data, and roadmap information. Essentially everything you see in Linear's interface is queryable through the AI agent.
Both. Read issue status, query velocity, and pull reports. Write operations include updating issue status, changing assignees, modifying labels, adjusting estimates, moving issues between projects, and bulk operations across multiple issues at once.
The MCP integration respects your Linear workspace permissions. You can only access and modify data you have rights to in Linear. Team-specific data remains isolated based on your access level.
No. Ask questions in plain English. The AI agent translates your prompts into Linear API calls, retrieves the data, and formats responses. You don't need to know GraphQL, field names, or API endpoints.
Every query pulls current data from Linear's API. If an issue status changed 10 seconds ago, your query reflects it. No caching delays, no stale dashboards. What you see in Linear is what the AI agent returns.
It complements it. Linear's insights are great for standard metrics. This excels at custom queries, cross-team comparisons, ad-hoc analysis, and questions that don't fit pre-built reports. Use both based on what you need.
Connect your data to an AI agent in under 60 seconds. The closed loop starts with one conversation.
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