# LinearB MCP for AI Agents AI Agent Connect

> LinearB lets you connect your engineering intelligence data to your AI agent. You can pull DORA metrics, track software deployments, and report incidents without leaving your chat interface. It turns raw engineering data into actionable insights for managers and DevOps teams who need to see the health of their delivery pipeline in real-time. Stop manual data entry and start using real-time engineering data to make better staffing and release decisions.

## Overview
- **Category:** ship-it
- **Price:** Free
- **Endpoint:** https://edge.vinkius.com/vk_preview_RQu5BgeMc4kEdSux6KvMTpIFSZDUWY8Nip6OyVZ1/ai-agent-connect
- **Tags:** dora-metrics, engineering-intelligence, deployment-tracking, incident-management, cycle-time, devops

## Description

LinearB lets you connect your engineering intelligence data to your AI agent. You can pull DORA metrics, track software deployments, and report incidents without leaving your chat interface. It turns raw engineering data into actionable insights for managers and DevOps teams who need to see the health of their delivery pipeline in real-time. Instead of hunting through multiple tabs to figure out why a release is lagging, you can just ask your agent for the numbers. This Connector lets your AI client pull real-time engineering metrics like cycle time and pickup time directly from the source. It handles the heavy lifting of reporting new deployments and incidents, so you don't have to manually update your tracking tools every time a build finishes or a bug hits production. It's about having a clear view of your delivery health without the manual data entry. When you use this through the Vinkius catalog, you get a direct line between your team's actual output and your AI's reasoning. You can audit organizational performance, map out team structures, and identify bottlenecks in your pipeline using plain language. It moves the needle from guessing how the team is doing to knowing exactly where the friction is.

## Tools

### record_new_deployment
Report a new deployment to LinearB by providing a repo ID and Git ref. This keeps your deployment history accurate and updated automatically.

### record_new_incident
Report a new engineering incident with a provider ID and start time. This helps your agent track your MTTR and Change Failure Rate metrics.

### list_software_deployments
List recent software deployments from your connected repositories. Use this to quickly see what was shipped and when.

### list_software_incidents
List all engineering incidents currently tracked in your account. This helps you audit stability and see recurring issues.

### query_software_metrics
Query software engineering metrics like cycle time or coding time for specific ranges. This gives you the raw data needed for performance audits.

### list_engineering_teams
List all teams defined in your LinearB workspace. Use this to organize your queries by specific departments or groups.

### list_connected_repos
List all the repositories currently connected to your LinearB account. This helps you map out which projects are being tracked by the agent.

## Prompt Examples

**Prompt:** 
```
What's the average cycle time for the Backend team this month?
```

**Response:** 
```
I've pulled the metrics for the **Backend** team for the last 30 days. Here is the summary:

| Metric | Average Value |
| :--- | :--- |
| **Cycle Time** | 3.5 days |
| **Coding Time** | 1.2 days |
| **Pickup Time** | 0.8 days |

The team is currently performing within the expected range, but you might want to look into the coding time to see if there are any blockers in the development phase.
```

**Prompt:** 
```
Report a new deployment for repo 123 with tag v2.1.
```

**Response:** 
```
I've successfully recorded the new deployment for repository **123**.

* **Git Ref:** `v2.1`
* **Status:** Recorded in LinearB

The cycle times for the Pull Requests included in this release will be updated automatically in your dashboard.
```

**Prompt:** 
```
List all active incidents for the last 48 hours.
```

**Response:** 
```
Here are the engineering incidents reported in the last 48 hours:

* **Incident #442:** Provider: *OpsGenie* | Started: *2026-07-10 09:15 AM*
* **Incident #445:** Provider: *PagerDuty* | Started: *2026-07-10 02:30 PM*

I can help you summarize these or check if they are still open.
```

## Capabilities

### Pull cycle time and coding metrics
Get real-time data on how long it takes your team to move code from start to finish.

### Report new software deployments
Automatically log new releases by feeding your agent the relevant Git refs.

### Log new engineering incidents
Report production issues instantly to keep your MTTR and failure rates accurate.

### List all connected repositories
See every project currently linked to your account in one view.

### View team structures and IDs
Map out your organizational structure to help your agent understand team hierarchies.

### Retrieve DORA performance data
Pull aggregated delivery metrics to identify bottlenecks in your pipeline.

## Use Cases

### Checking team velocity
A manager asks their agent to compare the average cycle time of the Backend team against the Frontend team for the last month.

### Automating release logs
A DevOps engineer tells their agent to report a new deployment for a specific repo using a Git tag, updating the history instantly.

### Audit production stability
A CTO asks for a list of all engineering incidents from the last 48 hours to see if the Change Failure Rate is trending up.

### Identifying delivery friction
A product lead uses the agent to find which repositories have the longest pickup times to identify where work is getting stuck.

## Benefits

- Get DORA metrics instantly. Use query_software_metrics to see how your delivery pipeline actually performs without opening a browser.
- Automate deployment logs. Use record_new_deployment to feed your agent the latest Git SHAs so your history stays current.
- Faster incident response. Use record_new_incident to log issues immediately, which helps calculate your MTTR automatically.
- Clearer team visibility. Use list_engineering_teams and list_connected_repos to give your agent the context it needs to answer questions about specific departments.
- Data-driven management. Stop guessing about bottlenecks and start using real cycle time and coding time data to make better staffing and release decisions.

## How It Works

The bottom line is that you get real-time engineering intelligence without the manual dashboard hopping.

1. Subscribe to the LinearB MCP in the Vinkius catalog.
2. Provide your LinearB Public API Key to authorize the connection.
3. Ask your agent to pull metrics, list teams, or report new deployments.

## Frequently Asked Questions

**Does LinearB MCP work with my existing DORA metrics?**
Yes, it connects directly to your LinearB account to pull and report DORA metrics like cycle time, deployment frequency, and MTTR.

**How do I use LinearB MCP to track deployments?**
You can simply tell your AI agent to record a new deployment by providing the repository ID and the Git reference like a tag or SHA.

**Can my AI agent report incidents automatically?**
Yes, your agent can log new incidents with provider details and start times, which helps keep your delivery health metrics accurate.

**What kind of metrics can I get from LinearB MCP?**
You can retrieve cycle time, coding time, pickup time, and other aggregated performance data for your engineering teams.

**Is LinearB MCP good for seeing team bottlenecks?**
It is excellent for this. By querying metrics across different teams, you can quickly identify where work is getting stuck.

**How do I connect my repositories to LinearB MCP?**
The Connector works with the repositories you have already connected to your LinearB account. It can list all of them for you automatically.

**How do I query cycle time for a specific team?**
Use the `query_software_metrics` tool and include the team name or ID in the `group_by` parameter of your JSON query.

**What is the difference between coding_time and pickup_time?**
Coding time is the duration from the first commit to the PR creation. Pickup time is the duration from the PR creation to the first review activity.

**Can I report a release from the agent?**
Absolutely. Use the `record_new_deployment` tool with the Git SHA or tag and the repository ID to inform LinearB that a deployment has occurred.