LaunchDarkly MCP server
Overview
This topic describes how to get started with the LaunchDarkly Model Context Protocol (MCP) server.
Model context protocol (MCP) is an open protocol that lets you interact with REST APIs using natural language. LaunchDarkly provides three hosted MCP servers, each focused on a different product area: feature management for managing feature flags, AI Configs for managing AI Configs and variations, and observability for querying logs, traces, errors, and dashboards. The APIs exposed by the MCP servers are called MCP tools.
Enable the MCP server
There are two options for enabling the LaunchDarkly MCP server:
We strongly recommend enabling the hosted MCP server. You can enable one, two, or all three hosted servers.
Use the MCP server
After you enable the LaunchDarkly MCP server following one of the procedures above, you can prompt your agent to create or manage your flags, AI Configs, and observability data. Typically you need to click Run tool or similar in your AI client to execute the result.
For example, you could try asking
Create a feature flag called “example feature” in my default project
or
Turn the “example feature” flag ON in all environments
or
Update the targeting rules for “example feature” so it’s only enabled for users in Canada
or
Show me error groups from the last 24 hours
For additional examples of prompts and responses, read the tutorial Create a feature flag in your IDE in 5 minutes with LaunchDarkly’s MCP server.
Available MCP tools
LaunchDarkly provides three hosted MCP servers. Each server exposes a focused set of MCP tools for a different product area.
Feature management server
The feature management server provides tools for feature flag management:
create-flag— Create a new feature flag in a project. Defaults to a boolean temporary flag. After creation the flag is OFF in all environments.get-flag— Get detailed configuration for a single feature flag scoped to a specific environment.list-flags— Search and browse feature flags in a project.toggle-flag— Turn a feature flag’s targeting on or off in a specific environment.update-flag-settings— Update a flag’s top-level settings: name, description, tags, temporary/permanent status, or maintainer.update-targeting-rules— Add, remove, or modify custom targeting rules for a flag in an environment.update-rollout— Change the default rule (fallthrough) for a flag.update-individual-targets— Add or remove specific users or contexts from individual flag targeting.copy-flag-config— Copy a flag’s targeting configuration from one environment to another.get-flag-status-across-envs— Check a flag’s lifecycle status across all environments.get-flag-health— Get combined health view for a single flag.find-stale-flags— Find feature flags that are candidates for cleanup.check-removal-readiness— Detailed safety check before removing a feature flag.manage-expiring-targets— List, add, update, or remove expiring targets on a flag.
AI Configs server
The AI Configs server provides tools for managing AI Configs and AI tools:
setup-ai-config— Create an AI Config with its first variation in one step.create-ai-config— Create a new AI Config in a project (config shell only).get-ai-config— Get detailed configuration for a single AI Config including all its variations.list-ai-configs— Search and browse AI Configs in a project.update-ai-config— Update an AI Config’s metadata: name, description, tags, or archive status.delete-ai-config— Permanently delete an AI Config.create-ai-config-variation— Create a variation for an AI Config (model, prompts, parameters, tools).update-ai-config-variation— Update an AI Config variation’s model, instructions/messages, parameters, or attached tools.clone-ai-config-variation— Clone an existing AI Config variation with selective overrides.delete-ai-config-variation— Permanently delete an AI Config variation.create-ai-tool— Create a new AI tool definition in a project.get-ai-tool— Get a single AI tool definition including its full schema.list-ai-tools— List AI tool definitions in a project.get-ai-config-health— Health check for an AI Config.
Observability server
The observability server provides tools for querying and visualizing observability data:
get-keys— Discover available data keys/dimensions for a product type.create-dashboard— Create a new empty dashboard for organizing charts.get-dashboard— Get detailed information about a specific dashboard.list-dashboards— List existing dashboards for the project.create-graph— Add a chart/graph to an existing dashboard.query-logs— Query project logs with date range and filters.query-traces— Query project traces with date range and filters.query-sessions— Query project sessions with date range and filters.query-error-groups— Query project error groups with date range and filters.query-metrics— Retrieve bucketed/aggregated metrics for a project.query-flag-evaluations— Query flag evaluations for a session.query-timeline-events— Query timeline indicator events for a session.
You can find the tools provided by the LaunchDarkly MCP server by reviewing the “Tools” list in your IDE:
Use agent skills with the MCP server
For complex, multi-step workflows, LaunchDarkly agent skills provide step-by-step procedures that guide the agent through the right sequence of tool calls.
Agent skills follow the agent skills open standard and work with any compatible editor such as Claude Code, Cursor, VS Code, Windsurf, and Codex.
Install agent skills
You can browse all LaunchDarkly agent skills at Vercel’s skills.sh.
To install agent skills:
How skills and MCP tools work together
Skills help AI agents facilitate common workflows, while MCP tools give the agent direct access to manage and understand your LaunchDarkly resources.
Example: Flag cleanup workflow
When you tell an AI agent to “clean up the old-checkout flag,” the launchdarkly-flag-cleanup skill guides the agent through:
- Discovery: the skill tells the agent to check flag status across all environments using the MCP server’s flag removal readiness tool
- Assessment: the tool returns a structured readiness classification (safe/caution/not-ready) with evidence
- Code references: the skill directs the agent to check where the flag appears in code
- Action: if safe, the skill guides the agent to make the code change, hardcoding the winning variation and opening a pull request (PR)
Without the skill, the agent might call the right tools but in the wrong order, miss the code reference check, or skip the readiness assessment. The skill encodes best practices so the agent follows the same procedure every time.
Additional resources
For additional information on the LaunchDarkly MCP server, or to file issues for our team, visit the LaunchDarkly MCP server GitHub repository.
