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Improvado gives your AI agent direct access to LaunchDarkly flag configurations, targeting rules, rollout percentages, and experiment results. Ask about any flag across any environment without opening the LaunchDarkly dashboard. Works with Claude, Cursor, and any MCP-compatible tool.
Stop hunting through environments and projects to check flag status. Ask your AI agent which flags are live, what targeting rules are active, and which experiments are running. The MCP server handles LaunchDarkly API calls across all projects and environments.
Your AI agent reads harmonized data across 500+ platforms. "Cost" in Google Ads and "spend" in Meta Ads resolve to the same field automatically.
Your AI agent can toggle flags, update targeting rules, and adjust rollout percentages directly. Routine flag operations that require navigating multiple screens happen in a single prompt.
250+ governance rules enforce naming conventions, budget limits, and KPI thresholds. SOC 2 Type II certified.
Set your AI agent to watch for stale flags, sudden targeting changes, and experiment results drifting outside expected ranges. Know before users notice.
Automated weekly reports, anomaly flagging, and budget alerts — all from a single conversation. No more morning check-ins across 5 dashboards.
Your AI agent can toggle flags, update targeting rules, and adjust rollout percentages directly. Routine flag operations that require navigating multiple screens happen in a single prompt.
Every phase runs through the same MCP connection. One protocol, all platforms, full governance. No switching between tools.
Hundreds of old flags accumulate across projects. No one knows which are still evaluated in production code, which are safe to archive, and which are secretly load-bearing. The audit takes days and still misses things.
Improvado extracts evaluation data alongside flag metadata. Ask your AI agent to identify flags with zero evaluations in the last 30 days across all environments. It cross-references creation date, last modification, and current targeting rules — producing a prioritized cleanup list in seconds.
A flag has slightly different targeting rules in staging vs. production. No one noticed when it was changed three sprints ago. Now a release is failing QA because behavior doesn't match what was tested. Tracking down the diff means manually comparing rule sets.
The MCP server makes environment comparison instant. Your AI agent pulls targeting rules from both environments and surfaces every discrepancy — which user segments differ, which percentage rollouts are misaligned, and which rule orderings changed.
Experiments run for weeks, results accumulate, and then nobody acts. The data is in LaunchDarkly but interpreting it means exporting CSVs, running stats manually, and writing up a summary that might get ignored anyway. Flags linger in experiment state indefinitely.
Ask your AI agent to summarize experiment results in plain language. It pulls conversion rates, confidence intervals, and sample sizes — then tells you whether the results are statistically significant and what action to take next.
Same MCP connection, different workflows for every team member. Agency CEOs get portfolio health. Media Strategists get campaign QA. Analysts get cross-platform reports. Account Managers get auto-generated QBR decks. Creative Directors get performance-based briefs.
Each role asks in natural language. The MCP server handles the complexity — rate limits, auth, schema normalization, governance — behind the scenes.
Feature flags (all configurations, targeting rules, rollout percentages, prerequisite flags), environments, projects, experiments and their results, segments, audit log entries, and flag evaluation metrics. Basically everything accessible through LaunchDarkly's REST API, queryable in natural language.
Both read and write operations are supported. You control the permission scope through LaunchDarkly's API token settings. Many teams configure read-only access for AI agents in production and full write access in staging environments. Write operations in production always return a confirmation step before executing.
Yes. The MCP server connects to all projects and environments your API token has access to. Cross-project and cross-environment queries work in a single prompt — for example, comparing flag states across dev, staging, and production simultaneously.
The most common use case is identifying stale flags. Your AI agent can query evaluation counts, creation dates, and last modification timestamps across all flags, then produce a prioritized list of candidates for archiving or removal. What used to take a manual audit taking days happens in under a minute.
Yes. Improvado stores all API credentials in an encrypted vault — SOC 2 Type II certified. Your AI agent sends queries through Improvado's secure proxy. Raw API keys never appear in conversation logs or prompt histories.
Under 5 minutes. Generate a LaunchDarkly API token, connect it to Improvado, then add one line to your Claude Desktop or Cursor MCP config. If you already use Improvado, your credentials may already be connected — check the integrations panel at app.improvado.io.
Connect your data to an AI agent in under 60 seconds. The closed loop starts with one conversation.
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