LaunchDarkly MCP. Check flag states across environments instantly.
LaunchDarkly MCP connects your AI client directly to feature flag systems, environments, and experiment data. You manage complex deployments by simply asking questions—checking if a new button is visible only in beta users' accounts or tracking how many people are engaging with the latest UI change.
Give Claude and any AI agent real-world access
The MCP retrieves the current on/off state of any feature flag within specified workspaces, like staging or production.
You can pull a list of every LaunchDarkly project associated with your account to understand your deployment scope.
The system gathers data on specific user engagement metrics, allowing you to evaluate A/B test performance directly from the chat.
It lists every environment (development, staging, production) connected to your main account for comprehensive oversight.
You can retrieve full audit log entries detailing who made changes and when they happened across the entire platform.
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What AI agents can do with LaunchDarkly with 9 Tools
Use these tools to list projects, check specific flags, retrieve metrics, and view audit logs for comprehensive deployment oversight.
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 LaunchDarkly MCPGet Environment
Fetches specific operational details about one particular workspace or environment.
Get Feature Flag
Provides deep, detailed information about one single feature flag by its name or ID.
Get Metric
Retrieves detailed data for a specific metric, helping you evaluate test performance.
Get Project
Fetches general configuration details for a specific LaunchDarkly project.
List Audit Logs
Retrieves an account-wide history of changes, showing who modified settings and when.
List Environments
Pulls every active environment (like Dev, Staging, or Production) within the current project.
List Feature Flags
Retrieves a comprehensive list of all feature flags defined in a specific project.
List Metrics
Gets a list of all available experimentation metrics within the current project...
List Projects
Retrieves a list of all available LaunchDarkly projects attached to your account.
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 LaunchDarkly, 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 LaunchDarkly. 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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No stored credentials
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Policy on each call
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The manual headache of release coordination
Right now, checking if a new feature is ready for launch feels like an archaeological dig. You jump from the main dashboard to the environment selector, then drill down into project settings, and finally find the specific flag status you need. If you're tracking A/B tests, you have to switch tabs again just to pull up the metric graphs. It’s slow, it requires constant context switching, and one missed click can delay a major rollout.
With this MCP, all that manual checking vanishes. You talk to your agent once—saying, 'What's the status of the checkout flag in Production?'—and you get a single, definitive answer, no clicks required.
LaunchDarkly provides instant visibility into deployment status
You eliminate jumping between project lists and environment selectors. You don't have to copy and paste flag names across three different screens just to confirm if the feature is live in QA, Staging, and Production.
Now you can use your agent to check any combination of flags or environments instantly. It cuts out the manual review process entirely, giving your team immediate confidence in every release.
What LaunchDarkly MCP does for your AI
Feature flags complicate releases; they’re what let developers roll out code piece-by-piece instead of flipping everything on at once. This MCP lets you talk to your LaunchDarkly platform and manage all those complex rules conversationally. You can check specific feature flag statuses across multiple environments, inspect which projects are active, or even pull historical audit logs to see who changed what and when.
Need to know if the new payment flow is live in Production but only for 10% of users? Your agent handles that query instantly. It's all managed through Vinkius, giving your AI client one place to access crucial deployment intelligence. You simply ask your agent to list available environments or check specific metrics without ever touching a dashboard.
019d75c5-1fe9-70b2-a46d-4b13bfa22517 How to set up LaunchDarkly MCP
The bottom line is you stop clicking through dashboards and start asking questions about your feature flags and deployments.
Install this MCP locally and supply your LaunchDarkly API token key.
Connect your AI client to the Vinkius catalog using the credentials.
Use natural language commands to request specific flag statuses, environment lists, or project metrics.
Who uses LaunchDarkly MCP
This MCP is essential for DevOps Engineers who hate manually checking release status, Product Managers needing instant confirmation on A/B test results, or Fullstack Developers whose jobs depend on knowing if a newly pushed flag actually went live.
You use this to monitor release rollouts and check deployment flags without needing to open multiple dashboards.
You confirm A/B test flag statuses immediately, letting you report on user engagement metrics from a single conversation.
You check if your newly pushed feature flag is live in the correct environment before merging code.
Benefits of connecting LaunchDarkly MCP
Cut out the dashboard hopping. Instead of clicking through multiple tabs to check a feature status, you just ask your agent and get the answer immediately, whether it's in Staging or Production.
Audit every change without searching logs for hours. Use list_audit_logs to pull an entire history, letting you see who made which flag modification and exactly when it happened.
Evaluate A/B tests right away. By calling list_metrics and get_metric, you stop guessing about user behavior and start basing decisions on real data.
Know your boundaries. You can use list_projects to map out every single deployment silo, making sure nothing gets accidentally missed during a release cycle.
Get immediate status checks. Whether you need the current state of a feature flag via get_feature_flag or just want to see all connected workspaces using list_environments, it's instant.
LaunchDarkly MCP use cases
The Production Flag Check
A Product Manager needs to know if the new checkout UI flag is active for beta users in Production. They ask their agent: 'Check the status of the checkout-beta-ui flag.' The MCP replies with the exact rollout percentage and environment details, saving them a 15-minute manual check.
The Post-Incident Investigation
A DevOps Engineer notices a bug and needs to know when the problematic configuration was deployed. They call list_audit_logs, retrieving a timeline that shows exactly which user changed the setting and what time it happened.
The Environment Map
A Fullstack Developer joins a new project and needs to know all available deployment targets. They ask for 'all connected workspaces,' triggering list_environments, instantly mapping out Staging, QA, and Beta nodes.
Comparing Feature Performance
The team is debating two different onboarding flows (A vs B). Instead of manually pulling graphs, they ask the agent to check 'onboarding-flow-metrics,' getting real-time data via list_metrics.
LaunchDarkly MCP tradeoffs
What to watch out for, and the recommended way to handle each one.
Assuming a flag is live
A developer sees the code merged and assumes the feature flag must be ON in Production. They waste time manually logging into dashboards, only to find no status indicator.
Instead of guessing, use get_feature_flag combined with list_environments. Your agent confirms the exact state (ON/OFF) for the specific environment you care about.
Ignoring deployment history
A bug pops up and no one remembers who changed the flag last week, so troubleshooting stalls while people hunt through Jira tickets.
Run list_audit_logs. This shows a clean timeline of every modification, telling you exactly when and by whom the setting was altered.
Focusing on code instead of flags
A developer pushes code and assumes it's ready for release. They forget that the flag might be accidentally disabled in the target environment.
Before deploying, always use get_environment to confirm the readiness status, followed by checking the specific feature flag with get_feature_flag.
When to use LaunchDarkly MCP
Use this MCP if your team's release process involves managing flags, multiple environments (Dev, Staging, Production), and continuous A/B testing. You need to know why a feature is visible or invisible, not just that the code exists. This connector excels at status reporting, auditing changes via list_audit_logs, and retrieving performance data through list_metrics.
Don't use this if your primary pain point is writing code logic or fixing deployment pipelines (you need CI/CD tools for that). If you only ever deal with a single environment and never change flags, then maybe the complexity isn't worth it. But if you operate in any modern product team managing releases, this MCP is critical because it gives your agent a complete view of all projects, environments, and flag statuses.
Frequently asked questions about LaunchDarkly MCP
How do I list all available deployments using LaunchDarkly MCP? +
You use list_projects to get an overview of all connected projects. Then, call list_environments to see the specific deployment targets (like Staging or Production) for that project.
Can I check flag status without knowing the exact flag name with LaunchDarkly MCP? +
First, use list_feature_flags to retrieve a comprehensive list of all available flags. Then, you can provide the specific name or ID when calling get_feature_flag.
Does LaunchDarkly MCP help with debugging who changed what? +
Yes, running list_audit_logs retrieves a full history of every change made across your account. This is crucial for understanding the sequence of events that led to an issue.
Is LaunchDarkly MCP only useful for Production environments? +
No, it works with all environments. You can use list_environments and get_environment to check status in Dev, Staging, or any other workspace you need to validate.
How does LaunchDarkly MCP help me evaluate A/B tests? +
You call list_metrics and then get_metric. This pulls specific user engagement data for the test flags, letting you see if one version is performing better than another.