Flightcontrol MCP for AI. Manage AWS deployments via conversation.
Gemini Works with every AI agent you already use
…and any MCP-compatible client
How this MCP server connects to your AI agent
Flightcontrol MCP lets your AI agent manage AWS infrastructure deployments from natural conversation. You can list projects, create environments, scale services, and trigger zero-downtime rollouts—all without leaving your IDE or chat window.
It gives your agent the power to orchestrate complex cloud resources directly.
What AI agents can do with Flightcontrol (AWS PaaS Deployments) Automation
Create aws account connection
Establishes a secure connection to a specific AWS account.
Create cloudfront invalidation
Forces a cache flush on CloudFront for content updates.
Create deployment
Initiates a full service deployment based on defined repository rules.
Automatically create new AWS projects or set up isolated staging environments within existing ones.
Change the number of running service instances instantly, adjusting capacity to meet current demand.
Execute controlled swaps between a blue and green environment to update services without any service interruption.
Get real-time updates on whether a deployment succeeded or failed, eliminating manual status checks.
Establish necessary AWS account connections and manage environment variables required for the services to run.
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What AI agents can do with Flightcontrol (AWS PaaS Deployments) with 24 Tools
These 24 tools let you perform every major action required to build, test, scale, and deploy applications across complex AWS environments.
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 Flightcontrol (AWS PaaS Deployments) on VinkiusCreate Aws Account Connection
Establishes a secure connection to a specific AWS account.
Create Cloudfront Invalidation
Forces a cache flush on CloudFront for content updates.
Create Deployment
Initiates a full service deployment based on defined repository rules.
Create Domain Group
Groups multiple domains together for certificate management.
Create Environment
Sets up a new, isolated operational environment within an existing project.
Create Environment Variables
Defines and sets necessary configuration variables for a service or environment.
Create Job Execution
Runs a specific, one-time background job using the scheduler service.
Create Project
Automates the setup and creation of an entirely new development project structure.
Create Service Variables
Sets variables specific to a service, separate from general environment settings.
Edit Environment
Modifies the configuration of an existing staging or testing environment.
Edit Preview Environment
Changes specific settings for a project's preview branch environment.
Get Aws Account Details
Retrieves general information and credentials for connected AWS accounts.
Get Cloudfront Invalidation Status
Checks the current status of a CloudFront cache invalidation request.
Get Deployment Status
Fetches the current state and progress details of any running deployment job.
Get Domain Details
Retrieves specific information about a single domain certificate.
Get Domains From Group
Lists all domains associated with a defined group of certificates.
Get Job Execution Status
Checks the final status and logs for a completed one-off job execution.
Get Service Scaling
Gets current metrics and limits related to how many instances a service can run.
Get Service
Retrieves detailed metadata about a specific service within a project.
List Projects
Lists all development projects that the team currently owns.
List Services
Retrieves a paginated list of all available services across your accounts.
Swap Blue Green
Switches the live traffic from an old environment (blue) to a newly updated one...
Trigger Deploy Hook
Starts deployments using a predefined secret URL endpoint.
Update Service Scaling
Adjusts the desired number of running instances for a service manually.
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.
Claude AI
Open Claude Settings
Go to claude.ai, click your profile icon, then navigate to Customize → Connectors.
Add Custom Connector
Click the "+" button and select Add custom connector. Paste your Vinkius endpoint URL:
https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp
Replace [YOUR_TOKEN_HERE] with your token
from cloud.vinkius.com. For OAuth-protected servers, expand
Advanced settings to add credentials.
Start a conversation
Open a new chat. The Flightcontrol integration is available immediately — no restart needed.
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 every call
- Real time usage dashboard and cost metering
- Publish to catalog or keep private
Make Your AI Do More
Start with Flightcontrol (AWS PaaS Deployments), then connect any of our 5,100+ other servers whenever your AI needs more. One click, no limits.
- Use this MCP plus 5,100+ others, all in one place
- Add new capabilities to your AI anytime you want
- Every connection is secured and compliant automatically
- Track usage and costs across all your servers
- Works with Claude, ChatGPT, Cursor, and more
- New servers added to the catalog every week
Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Flightcontrol. 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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Built on the Model Context Protocol (MCP) for Claude, ChatGPT, Cursor, and more
The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.
This connection provides 24 powerful capabilities that interface natively with Claude, ChatGPT, Cursor, and other compatible AI platforms. No middleware. No custom integration required.
Right now, updating infrastructure feels like a manual assembly job.
Today, changing an environment means logging into three different web consoles. You copy an ARN from one place, paste it into a configuration file in another, and then run the entire thing via CLI while keeping track of which tab is showing the actual logs. It's exhausting.
With this MCP, you just tell your agent what change needs to happen—for instance, 'Scale up service X.' The system handles all the necessary steps: checking current limits, running the scaling command, and confirming the new capacity, all in one conversation.
The Flightcontrol MCP gives you full control over deployment orchestration.
You no longer have to jump between `list_services` for an overview, then run `edit_environment` to fix a setting, and finally execute `create_deployment`. The agent sequences these steps automatically based on your prompt.
It’s not just about running commands; it's about managing the entire lifecycle. You can move from initial setup to zero-downtime swaps using tools like `swap_blue_green` without ever leaving your chat interface.
What your AI can actually do with this
Look, managing AWS resources used to mean jumping between the console, a dozen command-line tabs, and your local Git repository. Now, you can tell your AI agent what needs doing and watch it handle the complexity. This MCP lets you treat infrastructure management like writing code: declarative and conversational. You ask for an environment update, and the agent executes the required steps—from creating resource connections to scaling services and initiating a full deployment cycle.
It keeps everything running in one place. By connecting this capability through Vinkius, your preferred AI client gets access to this entire catalog of cloud actions, letting you focus on development while the system handles the heavy lifting.
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Here's how it actually works
The bottom line is: it takes complex cloud orchestration tasks and turns them into simple chat commands.
First, subscribe your AI agent to this MCP and provide the Flightcontrol API Key.
Next, ask your agent to perform a high-level task, like 'Create a staging environment for Project X.'
The agent translates that request into a sequence of calls, manages dependencies, and reports back the final state.
Who is this actually for?
This MCP is for the engineering teams that live in the gap between coding and operations. If your job involves checking deployment status, spinning up a staging environment, or scaling services during peak load times, you need this. It's built for people tired of context switching.
They use this to trigger deployments and audit project configurations across multiple AWS accounts without logging into the console.
They manage preview environments directly from their IDE, making sure staging mirrors production before a push.
They audit AWS account connections and VPC configurations through simple conversational queries to ensure compliance.
What Changes When You Connect
Zero-downtime updates become simple. Instead of complex load balancer adjustments, you just tell the agent to run swap_blue_green, and it handles routing traffic instantly.
You avoid losing context when debugging. If a deployment fails, running get_deployment_status gives you the full details right in your chat window, letting you fix issues immediately.
Setting up new services is faster than ever. Use create_project and then define necessary variables with create_environment_variables to get fully configured environments quickly.
Scaling capacity requires no manual effort. If traffic spikes, running update_service_scaling lets you adjust service instances instantly without touching the console.
Auditing becomes a single query. Use list_projects or get_aws_account_details to get an overview of every resource and connection point across your entire cloud footprint.
See it in action
The Staging Environment needs an update.
A developer finishes a feature branch. Instead of manually creating the staging environment, they ask their agent to run create_environment and populate it with variables using create_service_variables. The agent handles the setup, making sure the new code runs in isolation.
The service is getting slow under load.
An ops engineer notices performance dips. They ask their agent to check scaling limits via get_service_scaling, see that capacity is low, and then run update_service_scaling to provision more instances immediately.
We need a zero-downtime release.
The team pushes a major update. Instead of risking downtime by updating the live stack, they instruct their agent to first deploy to the green environment via create_deployment, and then execute the safe switch using swap_blue_green.
We are setting up a brand new microservice.
An infra lead needs to spin up an entire service. They first run create_project to define the boundary, then use get_aws_account_details to ensure the correct credentials are attached before proceeding.
The honest tradeoffs
Assuming everything is connected.
Trying to create an environment without first confirming the AWS account connection, leading to a vague permission error deep in logs.
Always check connectivity first. Use get_aws_account_details to verify your credentials are live before attempting any creation or deployment.
Skipping variable checks.
Running a deployment because the service is missing critical environment variables, causing it to fail only after hours of manual work.
Before deploying, ensure you run create_environment_variables and that all required settings are present in the target environment.
Relying on local knowledge for status.
Thinking a deployment is finished because the script ran without error, but failing to check if it actually passed health checks.
Never trust assumptions. Always run get_deployment_status to confirm that the service successfully reached its target state.
When It Fits, When It Doesn't
Use this MCP when you are managing a multi-step process involving infrastructure changes, deployment lifecycle management, or scaling actions. If your task requires more than two discrete CLI commands (like creating an environment AND setting variables AND deploying), this is the right tool. Don't use it just to look at status—if all you need is information (e.g., 'What domains are in this group?'), a simple read-only query might suffice, but if you need any action taken, this MCP provides the control.
Questions you might have
How do I use create_project with Flightcontrol MCP? +
You ask your agent to 'Create a new project named X.' The tool automates the entire setup, giving you an isolated workspace ready for development.
What is the difference between create_environment and edit_environment using Flightcontrol MCP? +
Use create_environment to set up a completely new staging area. Use edit_environment when you need to modify settings on an environment that already exists.
Can I check deployment status with get_deployment_status using Flightcontrol MCP? +
Yes, running get_deployment_status tells you exactly where a deployment is in the process—whether it's pending, deploying resources, or if it failed.
How does swap_blue_green work with Flightcontrol MCP? +
This tool safely switches live traffic from an old version (the blue environment) to a new one (the green environment), ensuring zero downtime during the rollout.
How do I check or establish AWS credentials using get_aws_account_details and create_aws_account_connection? +
The MCP handles connection setup by first creating an account link via create_aws_account_connection. Then, you use get_aws_account_details to confirm the connectivity status and verify your region parameters. This ensures your agent has the necessary permissions before any deployment actions run.
What information does get_service_scaling provide regarding service capacity? +
get_service_scaling retrieves the current scaling metrics for a specific service instance. It details minimum and maximum allowed replicas, helping you determine if update_service_scaling needs to be run before your deployment.
If an automated job fails, how do I check its status with get_job_execution_status? +
get_job_execution_status provides a direct report on background jobs. It returns the current state—running, succeeded, or failed—and includes necessary logs to pinpoint exactly where the execution broke down.
After deploying code, how do I clear cached assets using create_cloudfront_invalidation? +
Use create_cloudfront_invalidation immediately after a deployment. This tells Amazon CloudFront to discard old versions of your site's files and pull the latest content from your origin server.
Can I see all my active projects and their IDs? +
Yes! Use the list_projects tool to retrieve a complete list of projects owned by your team, including their unique identifiers and repository links.
How do I check the configuration of a specific service? +
Simply provide the Service ID to the get_service tool. Your agent will fetch the full details, including type, status, and current environment mapping.
Is it possible to scale my services using the AI? +
Yes, the update_service_scaling tool allows you to manually adjust the scaling parameters of your services directly through the conversation.
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