LinearB MCP. Query metrics and track deployments via your AI client.
LinearB connects your AI agent directly to your software delivery pipeline, automating engineering intelligence and DORA reporting. You can query complex metrics like cycle time or coding duration across multiple teams. It also allows you to report new deployments using Git references and log incidents to accurately calculate MTTR and Change Failure Rate.
Give Claude and any AI agent real-world access
Ask about complex metrics like average cycle time, coding duration, or pickup time across specific teams.
Inform the system about a new software deployment by providing a Git reference (SHA or tag).
Record and list engineering incidents, which is necessary for calculating Mean Time To Recover (MTTR) and Change Failure Rate.
View a comprehensive list of all connected repositories and defined engineering teams in the system.
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What AI agents can do with LinearB: 7 Tools for Delivery Intelligence
These tools allow your agent to perform specific actions within LinearB, such as listing teams or recording a new software incident. Use them to gather structured data instantly.
Make your AI actually useful.
Add this MCP to Claude, Cursor, or Windsurf and your AI stops guessing. It gets real tools to look things up, take action, and handle the stuff you keep doing by hand.
Start using LinearB MCPRecord New Deployment
Reports a new software release into LinearB using a specific repository ID and Git reference.
Record New Incident
Creates an incident record for service outages, requiring the provider ID and time...
List Software Deployments
Retrieves a list of all recent software deployments recorded in LinearB.
List Software Incidents
Fetches a listing of engineering incidents to track service disruptions.
Query Software Metrics
Queries detailed software engineering metrics, allowing you to specify what data...
List Connected Repos
Retrieves a list of all repositories that have been connected and monitored by LinearB.
List Engineering Teams
Lists every team defined within the LinearB system for scope management.
Security and governance baked right in.
Pick your AI client below to get set up. Just create a Vinkius account, subscribe, and you're instantly up and running. We handle the entire backend infrastructure, delivering out-of-the-box support for HTTPS Streamable, SSE, and OAuth2—zero messy routing required.
Choose How to Get Started
Build a custom MCP for your own tools, or connect a ready-made integration from our catalog.
Build Your Own
Turn any API into an MCP. Import a spec, define Agent Skills, or deploy with MCPFusion.
- Import from OpenAPI, Swagger, or YAML specs
- Create Agent Skills with progressive disclosure
- Deploy to edge with MCPFusion framework
- Built in DLP, auth, and compliance on each call
- Real time usage dashboard and cost metering
- Publish to catalog or keep private
Make Your AI Do More
Start with LinearB, then connect any of our 5,200+ other servers whenever your AI needs more. One click, no limits.
- Use this MCP plus 5,200+ others, all in one place
- Add new capabilities to your AI anytime you want
- Connections are secured and governed automatically
- Track usage and costs across all your servers
- Works with Claude, ChatGPT, Cursor, and more
- New servers added to the catalog weekly
Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by LinearB. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.
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Tracking software health usually means switching between five different tabs.
Right now, tracking delivery performance involves constant context switching. You check the deployment dashboard for recent releases; then you switch to the team management view to see who owns which repo. Next, you open a separate tab just to list out all current incidents and manually calculate the MTTR by looking at timestamps. It's a tedious process of clicking through dashboards and copy-pasting data into summary documents.
With this MCP, that whole routine vanishes. You tell your agent what you need—whether it’s querying cycle time or listing team structures. The agent handles the clicks across all those services behind the scenes. You get one clean answer: a comprehensive view of your engineering performance without touching a dashboard.
LinearB MCP gives you immediate control over incident and deployment tracking.
The biggest manual steps that go away are the need to manually log releases or track incidents. You no longer have to stop your workflow to navigate a UI just to record a new deployment; you simply tell your agent to use `record_new_deployment`. Similarly, logging an outage is as simple as using the `record_new_incident` tool.
It's not about viewing data anymore. It's about controlling the inputs that generate the metrics. You own the historical record of deployments and incidents instantly.
What LinearB MCP does for your AI
Managing software health used to mean opening a dozen dashboards—one for deployments, one for team capacity, another for incident logs. Now, your agent handles the heavy lifting. This MCP lets your AI client access all those critical engineering metrics directly. You can ask natural language questions like, 'What was our average cycle time last month?' and get an immediate answer detailing coding time versus pickup time.
Need to log a new release? Your agent records that deployment using the Git reference, keeping your records current without you lifting a finger. If something breaks, reporting a new incident is just a command away. Because this capability lives in the Vinkius catalog, connecting it takes minutes. You get a single source of truth for performance data—the whole picture needed to audit organizational health and track deployments.
019d75c7-9e9e-7357-afef-75d61374aa2c How to set up LinearB MCP
The bottom line is that you talk to your agent like talking to a coworker; it handles all the API calls and data formatting for you.
Subscribe to this MCP and provide your LinearB Public API Key.
Authorize your AI client to connect to the Vinkius catalog.
Use natural language commands within your agent to query metrics, list deployments, or report incidents.
Who uses LinearB MCP
This MCP is built for technical leaders and operational engineers. If you're an Ops Engineer tired of clicking through five different dashboards at 2 AM, or a CTO who needs to audit performance without opening the web app, this is for you.
Automates logging deployments and incidents directly from CI/CD tools or IDEs so metrics stay accurate.
Gets a quick readout on team cycle times or overall delivery health using simple natural language commands.
Runs ad-hoc audits on organizational performance and DORA metrics without needing to navigate the full dashboard interface.
Benefits of connecting LinearB MCP
You can query complex engineering data, like average cycle time across teams, simply by asking a question. This is done using the query_software_metrics tool, eliminating manual dashboard report generation.
Need to keep deployment records current? You use record_new_deployment to tell LinearB about a new release using its Git reference, ensuring your DORA metrics never suffer from stale data.
When an outage happens, you immediately call the record_new_incident tool. This action logs the event and is critical for calculating accurate Mean Time To Recover (MTTR).
You don't need to manually map out who owns what. You can use list_engineering_teams and list_connected_repos to quickly understand your entire technical structure.
The ability to list both deployments (list_software_deployments) and incidents (list_software_incidents) in one flow gives you a full, chronological picture of system stability.
LinearB MCP use cases
Auditing Team Performance
A manager needs to know if the 'Backend' team is falling behind. Instead of opening the dashboard and clicking filters for time ranges and metric types, they ask their agent: 'What was the average cycle_time for Backend over the last 30 days?' The agent uses query_software_metrics and immediately reports the data points.
Logging a Critical Outage
The primary service goes down. An engineer opens their chat client and tells their agent: 'Record an incident for OpsGenie starting now.' The agent uses record_new_incident so that the MTTR calculation starts instantly, without manual data entry.
Tracking a Major Release
The CI/CD pipeline finishes and pushes version v2.1.0 for repo 456. Instead of navigating to LinearB's UI, the DevOps engineer asks their agent: 'Report deployment v2.1.0 for repo 456.' The agent uses record_new_deployment instantly.
Understanding System Scope
A new team member joins and needs to understand the overall architecture. They ask their AI client: 'List all connected repositories and teams.' This triggers both list_connected_repos and list_engineering_teams, giving them a complete map.
LinearB MCP tradeoffs
What to watch out for, and the recommended way to handle each one.
Copy-pasting metrics
The user manually opens the LinearB dashboard, copies cycle time data for Q3 into a spreadsheet, and pastes it into an email summary.
Just ask your agent. Use query_software_metrics to request the specific range and metric you need directly in natural language. The metrics come straight through.
Forgetting deployment details
A developer finishes a feature branch, but forgets to manually update the system with the Git SHA, leading to gaps in DORA reporting.
Make it part of your process. Use record_new_deployment immediately after merging. This ensures every release is logged automatically.
Manual incident tracking
When an outage happens, the team starts a thread in Slack and manually updates spreadsheets with timestamps and affected providers.
Use record_new_incident immediately. This action logs the necessary start time against your provider ID, starting the clock on MTTR.
When to use LinearB MCP
Use this MCP if your core problem is synthesizing performance data from multiple sources into actionable insights, especially regarding software delivery metrics (DORA). It excels at automating logging—like recording deployments or incidents—and complex querying. Don't use this if you simply need to view a single dashboard chart; the agent will still pull that data for you via query_software_metrics. Conversely, don't expect it to handle ticket prioritization or communicate with external ticketing systems (like Jira). It lives purely in the realm of engineering performance tracking and logging. If your goal is 'What happened?' or 'How fast?', this MCP works. If your goal is 'Fix this problem,' you need a different tool.
Frequently asked questions about LinearB MCP
How do I query cycle time using LinearB MCP? +
You ask your agent directly, specifying the metric and time frame. The query_software_metrics tool handles the complex data request, giving you immediate insight into coding time versus pickup time.
Can I use LinearB MCP to track deployments from CI/CD? +
Yes. You can use your agent to trigger record_new_deployment by passing the Git SHA or tag, ensuring that every release is logged automatically for accurate reporting.
What happens when I list engineering teams with LinearB MCP? +
The list_engineering_teams tool fetches a clean list of all defined teams in the system. This helps you map technical IDs to specific organizational units for better reporting.
Does LinearB MCP help calculate MTTR? +
Yes, by using record_new_incident, your agent logs the start time of an incident against a provider. This critical data point allows you to accurately track and calculate Mean Time To Recover (MTTR).
Which repositories can I query with LinearB MCP? +
First, use list_connected_repos to see all available sources. Then, your agent uses those IDs when calling tools like query_software_metrics or record_new_deployment.