linear · MCP Server

Linear + Improvado MCP — Sprint Health at a Glance

Improvado's MCP server connects Linear to Claude, Cursor, and other AI agents. Query your Linear data in natural language — no manual exports or API scripts required.

46K+ metrics ·Read & Write access ·500+ platforms ·<60s setup

Read

Query Linear data in natural language

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.

Example prompts

"High priority issues still in triage this cycle"

10 min → 10 sec

"Average cycle time for shipped issues this quarter"

20 min → 15 sec

"Projects with most overdue issues — who owns them?"

15 min → 10 sec

Works with Claude ChatGPT Cursor +5

Write

Update issues and cycles through conversation

Triage issues, update statuses, adjust estimates, and modify labels. Bulk operations that would take 20 clicks happen in one prompt.

Example prompts

"Move all unestimated issues in the design system project to backlog"

5 min → 10 sec

"Add the 'scope-creep' label to any issue created after cycle start"

10 min → 15 sec

"Update all blocked issues to mention the dependency in comments"

8 min → 10 sec

Every action logged · Fully reversible · SOC 2 certified

Monitor

Set up alerts for what matters

Track cycle health, velocity trends, and team capacity. Get notified when scope creeps, SLAs slip, or blockers pile up.

Example prompts

"Alert if cycle scope grows 20%+ after kickoff"

Manual → auto

"Flag high priority issues in triage over 2 days"

Manual → auto

"Track frontend team estimate changes week over week"

Manual → auto

Alerts sent to Slack, email, or your AI agent

Full cycle

Close the loop on Linear workflows

Your AI agent doesn't just surface data — it acts. Adjust pricing, update product descriptions, manage inventory, apply discounts — all through natural language. The MCP server translates intent into API operations.

Every phase runs through the same MCP connection. One protocol, all platforms, full governance. No switching between tools.

Ideate

Launch

Measure

Analyze

Report

Iterate

One conversation. All six phases. Every platform.

The daily grind

Common problems. Direct answers.

Challenge 1

Velocity comparison across teams

The problem

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.

How MCP solves it

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.

Try asking

Compare velocity across all engineering teams this quarter, normalized for cycle length

Answer in seconds

All data sources, one query

Challenge 2

Mid-cycle scope creep detection

The problem

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.

How MCP solves it

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.

Try asking

Show me all issues added to current cycles after kickoff, grouped by who created them

Full detail preserved

No data loss on export

Challenge 3

Executive status rollups

The problem

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.

How MCP solves it

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.

Try asking

Summarize all active projects: completion %, blockers, and at-risk items for exec review

Unified data model

Compare anything side by side

👥 Teams

One Framework. Five Roles. Zero Setup.

Same MCP connection, different workflows for every team member. Each role asks in natural language — the MCP server handles the complexity (rate limits, auth, schema normalization, governance) behind the scenes.

Agency CEO

Portfolio health. Client risk. Revenue signals.

Media Strategist

70% strategy, not 70% ops. Auto campaign QA.

Marketing Analyst

Zero wrangling. Cross-platform. AI narratives.

Account Manager

QBR decks auto-generated. Call prep in 30s.

Creative Director

Performance-to-brief. Predict winners before spend.

FAQ

Common questions

What Linear data can I access through MCP?

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.

Can I update Linear data or just read it?

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.

How does this handle Linear's workspace permissions?

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 — all through Improvado's hosted MCP server.

Do I need to know Linear's API to use this?

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.

How real-time is the data?

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.

Can this replace Linear's built-in reporting?

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.

Stop Reporting. Start Executing.

Connect your data to an AI agent in under 60 seconds. The closed loop starts with one conversation.

SOC 2 Type II GDPR 500+ Platforms