Govern Feature Flags Across Tools Using MCP.
127 feature flags in production and nobody knows which ones are safe to remove , your agent audits both platforms and tells you
Works with every AI agent you already use
…and any MCP-compatible client
Waiting for input…
Gemini How It Works
Your AI agent queries LaunchDarkly for all feature flags , name, status, last evaluated, targeting rules, environments. Then it queries ConfigCat for the same.
It cross-references: are any flags defined in both platforms with different values? Are there flags that have not been evaluated in 90 days? Flags still targeting 100% of users that should have been cleaned up after the rollout? For each finding, the agent creates or updates a record in Airtable: flag name, platform, status, last evaluation date, days since last evaluation, risk level, recommended action.
A flag that has not been evaluated in 180 days and still has complex targeting rules gets tagged 'STALE , remove.' A flag present in both LaunchDarkly and ConfigCat with different boolean values gets tagged 'CONFLICT , reconcile.' The Airtable base becomes your flag governance dashboard.
Connector Orchestration: 3 Connectors, one intelligent agent
Connect LaunchDarkly, ConfigCat and Airtable Connectors so your AI agent audits feature flags across both platforms, identifies stale flags, conflicting configurations and governance gaps, then logs everything to an Airtable base for tracking. Teams running feature flags in two platforms who discover a 3-year-old flag still controlling production traffic get a weekly audit instead of a quarterly panic.
Launchdarkly
triggerLists flags, evaluations and targeting rules across environments
list_feature_flags get_feature_flag list_environments list_projects Configcat
enrichmentPulls feature flag settings from the secondary flag management platform
list_settings get_setting get_setting_value list_environments Airtable
actionLogs audit results, stale flags and remediation tasks
create_records update_records list_records search_records Run This Automation Today
Connect Claude, ChatGPT, Cursor, or any AI agent to the Vinkius catalog and run this automation in minutes.
Build Your Own Connector
Convert any internal API into a Connector. 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
Connect & Automate
The 3 servers this recipe uses are ready in the catalog. Connect them once, paste a prompt, and your AI runs the full workflow.
- Launchdarkly, Configcat & Airtable ready in the catalog right now
- Add more from 5,800+ servers whenever you need
- Connections are secured and compliant by default
- Track usage and costs across all your servers
- Works with Claude, ChatGPT, Cursor, and more
- New servers and recipes added weekly
Superpowers you didn't know your AI had
The Vinkius catalog gives your agent access to 5,800+ Connectors and the intelligence to combine them. Imagine never logging into another dashboard. Your AI handles the work across all tools, in one conversation. That's what this connectivity layer was built for.
Cross-Platform Intelligence
Your agent doesn't just connect to tools. It understands the relationships between them. Data flows where it needs to go, automatically, with full context preserved across all platforms.
Contextual Reasoning
Each decision your agent makes considers the full picture. It reads CRM data, checks calendars, reviews conversation history, and acts on everything at once. Not step by step. All at once.
Productivity at Scale
What used to take 45 minutes across five different dashboards now takes one sentence. Your agent runs the entire workflow end to end while you focus on decisions that actually matter.
Zero-Config Reliability
No API keys to paste. No webhooks to configure. No YAML to debug. Connect your Connectors once, and your agent handles the rest. Each time, without intervention.
Made for
exactly this
Your AI agent taps into the entire Vinkius AI Connectors to handle these for you. You describe what you need. It does the rest.
Platform teams managing 50+ feature flags across LaunchDarkly and ConfigCat who need a unified audit without manual CSV exports
Engineering managers who want a weekly stale flag report to keep technical debt visible and actionable
Teams migrating from ConfigCat to LaunchDarkly (or vice versa) who need to detect drift between platforms during the transition
Compliance-conscious teams in fintech or healthcare who need documented evidence of feature flag governance for audits
Frequently Asked Questions About This Connector Orchestration
Which Connectors do I need for this workflow?
Three: LaunchDarkly, ConfigCat and Airtable. Connect all three to your AI client before running any prompt from this page.
Does this work with Claude Desktop, Cursor or Windsurf?
Yes. Any AI client that supports the Model Context Protocol works , Claude Desktop, Cursor, Windsurf, Cline and others. Connect the Connectors and paste a prompt.
What if I only use one flag platform?
Drop the second MCP. The stale flag audit and Airtable logging work with just LaunchDarkly or just ConfigCat. The cross-platform conflict detection requires both.
How does the agent determine 'stale'?
By last evaluation timestamp. Flags not evaluated in 90+ days are stale. You can adjust the threshold in your prompt , 'flag anything not evaluated in 60 days' works too.
Is my flag configuration data secure?
Connectors authenticate through API keys. Your flag data stays in LaunchDarkly and ConfigCat. The Airtable base is in your workspace. Vinkius does not store your configurations.
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Connectors used in this workflow
LaunchDarkly
LaunchDarkly lets you manage feature flags, environments, and experiments through your AI client. Instead of digging through a dashboard, you can ask your agent to check if a flag is live, see who's seeing a new feature, or grab the latest deployment metrics. It connects your release management process directly to your chat interface, making it way faster to toggle features and monitor production health without switching tabs.
ConfigCat
ConfigCat MCP for AI Agents. Manage feature flags and remote configurations from your AI client. Create environments, toggle features, and manage user segments without leaving your editor or chat interface.
Airtable
Airtable MCP connects your Airtable bases to your AI agent. It lets you query records, edit spreadsheets, and build automated workflows through chat. Instead of clicking through rows, you just tell your agent what to do.