Flightcontrol MCP. Manage every AWS resource change through conversation.
Works with every AI agent you already use
…and any MCP-compatible client
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Flightcontrol (AWS PaaS Deployments) MCP Server manages and automates AWS infrastructure directly from your AI client. Use it to list projects, create new environments, update service scaling, and manage VPCs without leaving your IDE.
Trigger deployments, check status, and manage full service lifecycles using natural conversation.
What your AI agents can do
Create aws account connection
Establishes a new AWS account connection for the project.
Create cloudfront invalidation
Invalidates the CloudFront cache for a specified domain.
Create deployment
Triggers a new deployment based on filters from a Git repository.
List all existing projects or automate the setup of a new project, including integrating a Git repository.
Create, edit, or manage environment settings—like standard or preview environments—for any service within a project.
Retrieve current service scaling information and manually adjust the number of service instances needed.
Trigger a full deployment from a Git repository and perform controlled traffic swaps between Blue and Green environments.
Check AWS account details and manage VPC configurations to ensure all required infrastructure is connected and compliant.
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Flightcontrol MCP Server: 24 Tools for AWS PaaS Management
Use these tools to manage every step of your cloud deployment pipeline, from creating projects to swapping live traffic.
019e5d1acreate aws account connection
Establishes a new AWS account connection for the project.
019e5d1acreate cloudfront invalidation
Invalidates the CloudFront cache for a specified domain.
019e5d1acreate deployment
Triggers a new deployment based on filters from a Git repository.
019e5d1acreate domain group
Creates a group to manage multiple domain certificates.
019e5d1acreate environment
Provisions a new, isolated environment within a project.
019e5d1acreate environment variables
Creates and sets environment variables for a specific service or environment.
019e5d1acreate job execution
Starts a one-off background job in a scheduler service.
019e5d1acreate project
Automates the setup and creation of a new project.
019e5d1acreate service variables
Sets up variable definitions for a service.
019e5d1aedit environment
Modifies the settings of an existing service environment.
019e5d1aedit preview environment
Changes the settings of a project's preview environment.
019e5d1aget aws account details
Retrieves the current details of the AWS account connection.
019e5d1aget cloudfront invalidation status
Checks the status of a CloudFront cache invalidation request.
019e5d1aget deployment status
Checks the current status and logs of a running deployment.
019e5d1aget domain details
Retrieves detailed information for a single domain name.
019e5d1aget domains from group
Lists all domains contained within a specific certificate group.
019e5d1aget job execution status
Checks the status and output of a scheduled background job.
019e5d1aget service
Fetches specific operational details for a named service.
019e5d1aget service scaling
Retrieves the current instance count and scaling configuration for a service.
019e5d1alist projects
Lists all projects currently managed by the team.
019e5d1alist services
Gets a paginated list of all available services.
019e5d1aswap blue green
Swaps traffic between the Blue and Green environments for a service.
019e5d1atrigger deploy hook
Initiates a deployment using a secret URL hook.
019e5d1aupdate service scaling
Manually adjusts the number of running instances for a service.
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What you can do with this MCP connector
Flightcontrol (AWS PaaS Deployments) MCP Server manages your AWS infrastructure directly from your AI client. You'll use this server to list projects, create new environments, update service scaling, and manage VPCs without ever touching the AWS console. You can trigger deployments, check status, and manage the full service lifecycle just by talking to your agent.
Manage Project Structure
- You can list all projects using
list_projects. You'll also automate the setup of a new project, including connecting a Git repository, by callingcreate_project. - You manage service environments by using
create_environmentto provision a new, isolated workspace, or by callingedit_environmentto modify an existing service environment. You can also change settings for a project's preview environment usingedit_preview_environment.
Service Operation
- You get specific operational details for a service using
get_service. You can also check the current instance count and scaling setup withget_service_scaling. - You manually adjust the number of running service instances using
update_service_scaling. You'll also set up variable definitions for a service withcreate_service_variables, or create and set environment variables for a specific service or environment viacreate_environment_variables. - You can get a paginated list of all available services using
list_services.
Deployment and Traffic Control
- You trigger a full deployment from a Git repository by calling
create_deployment, or initiate a deployment using a secret URL hook viatrigger_deploy_hook. - You monitor the status and logs of any running deployment with
get_deployment_status. - You perform controlled traffic swaps between the Blue and Green environments for a service using
swap_blue_green.
Infrastructure and Networking
- You establish a new AWS account connection for your project using
create_aws_account_connection, and you can retrieve the current AWS account details withget_aws_account_details. - You manage domain certificates by using
create_domain_groupto create a group for multiple domain names, and you can list all domains in that group withget_domains_from_group. You'll also get detailed info for a single domain withget_domain_details. - You can run a one-off background job in a scheduler service using
create_job_execution, and check its status and output withget_job_execution_status. - You can also create a group to manage multiple domain certificates, and you'll use
create_domain_groupandget_domains_from_groupfor this.
How Flightcontrol MCP Works
- 1 Subscribe to the server and provide your Flightcontrol API Key.
- 2 Your AI client connects to the server, authorizing access to your AWS account.
- 3 You issue a natural language command (e.g., 'Deploy the latest commit to staging for project X'). The agent executes the necessary tools to complete the task.
The bottom line is, you manage all your AWS PaaS deployments from a single conversation, never needing to switch tools or log into the AWS console.
Who Is Flightcontrol MCP For?
The DevOps engineer who gets tired of clicking through AWS dashboards at 2 AM. The full-stack developer who needs to test staging environments while coding. Infrastructure Leads who must audit complex project configurations quickly. If your job involves managing microservices across multiple AWS environments, this saves hours of manual CLI work.
Uses get_deployment_status to check deployment progress and create_service_variables to update config without writing a script.
Uses create_environment to spin up a new preview environment for testing, and then edit_preview_environment to tweak settings before committing.
Uses list_projects to audit the entire portfolio, and get_aws_account_details to verify core connections.
What Changes When You Connect
- Manage project structure by listing all projects with
list_projectsor automating a new one withcreate_project. You never lose track of where a service lives. - Control service scaling immediately. Instead of digging into dashboards, use
get_service_scalingto check current instances, thenupdate_service_scalingto scale up or down. - Guarantee zero downtime releases using
swap_blue_green. It handles the traffic shift, giving you a safe, controlled way to go live. - Audit AWS connections and VPCs quickly. Use
get_aws_account_detailsandlist_projectsto verify project compliance without manual checks. - Maintain environment parity. You can
create_environmentand thenedit_environmentto ensure staging always matches production config. - Track deployments end-to-end. Use
create_deploymentto start a release, andget_deployment_statusto track it until completion.
Real-World Use Cases
Need to check if the latest release worked?
The QA team finishes testing and needs to verify the deployment status. They tell their agent: 'What's the status for project X?' The agent uses get_deployment_status and reports back the final state. If it fails, the team uses get_service to see which specific service failed and why, solving the issue instantly.
A feature needs to go live, but we can't risk downtime.
The product manager approves the release. The engineer asks the agent to run the release: 'Swap the traffic for the main API to Green.' The agent executes swap_blue_green and confirms the controlled rollout, minimizing risk and zeroing downtime.
The staging environment variables are out of date.
A developer notices a config drift. They tell the agent to fix it: 'Set the API key variable in the staging environment.' The agent uses create_environment_variables and edit_environment to update the configuration instantly, ensuring parity with production.
We need to test a service before the full project is ready.
An engineer only needs to test a component. They ask the agent to create a temporary workspace: 'Create a new preview environment for service Y.' The agent runs create_environment and edit_preview_environment, giving the developer a safe sandbox to work in.
The Tradeoffs
Manual AWS Console Navigation
Logging into the AWS console, navigating to the service, finding the environment, and clicking 'Deploy' multiple times. This takes 15 minutes and requires jumping between 5 different tabs.
→
Tell your agent: 'Deploy the latest commit to staging for project X.' The agent handles the entire sequence using create_deployment, and you just monitor the progress with get_deployment_status.
Running scripts with stale variables
Writing a complex bash script that assumes environment variables are set, but the variables were never updated in the cloud console, causing the deployment to fail hours later.
→
First, use get_service_scaling to confirm the service is ready. Then, use create_environment_variables to update the required settings before running create_deployment.
Ignoring domain dependencies
Trying to set up a new domain name for a service without first creating the domain group, leading to certificate errors and deployment failure.
→
First, run create_domain_group to establish the certificate group. Then, use get_domain_details to ensure the new domain is correctly registered before any other deployment tools run.
When It Fits, When It Doesn't
Use this server if your team manages microservices, needs to deploy across multiple environments (dev, staging, prod), or relies heavily on AWS PaaS features. You need a single source of truth for the entire deployment lifecycle.
Don't use this if you only manage simple, non-AWS services, or if your infrastructure changes are purely manual (e.g., physical hardware). If you just need to check a single resource, some tools might suffice, but the full suite is better. Use a simple scripting language if you only need to list projects (list_projects). But if you need to change anything—scaling, variables, environments—you need this full toolset.
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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Works with 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 server provides 24 capabilities that interface natively with Claude, ChatGPT, Cursor, and any MCP client. No middleware. No custom integration required.
Available Capabilities
Managing complex cloud deployments shouldn't mean opening 10 different tabs.
Today, updating a service requires a painful manual dance. You log into the AWS dashboard, find the service, check the current environment settings, and copy-paste environment variables into a separate console. If you miss a single click or copy the wrong variable, the deployment fails, and you waste an hour debugging the console.
With this MCP server, you just talk to your agent. You tell it exactly what you want—'Deploy the new feature to staging.' The agent executes the necessary `create_environment_variables`, `create_deployment`, and checks the status, all without you touching the dashboard. It just works.
Flightcontrol MCP Server: Zero-Touch Service Control
Manual deployments involve checking scaling limits, manually swapping traffic, and running multiple scripts. Now, you tell your agent to swap the environment: 'Swap the main API to Green.' The agent runs `swap_blue_green` and manages the complex traffic shift. You get reliable, controlled rollouts, period.
This isn't just a faster way to click buttons. It gives you transactional control over your entire service lifecycle, guaranteeing that the state you *want* is the state you *get*.
Common Questions About Flightcontrol MCP
How do I use the create_deployment tool? +
You trigger a deployment by naming the tool and providing the necessary repository filters. The agent handles the actual deployment process and gives you a job ID to track it.
What is the difference between create_environment and edit_environment? +
Use create_environment when you need a brand new environment (e.g., 'staging'). Use edit_environment when the environment already exists, and you just need to change its settings.
Can I check the status of a deployment using get_deployment_status? +
Yes, get_deployment_status is designed for this. You give it the deployment ID, and it reports the current status and any logs immediately.
How does swap_blue_green work? +
The swap_blue_green tool performs a controlled traffic shift. It swaps the active environment from Blue to Green (or vice versa), ensuring zero downtime during the switch.
Do I need to run create_aws_account_connection first? +
Yes, you must establish the connection first. create_aws_account_connection links the server to your AWS account, which is the prerequisite for all other infrastructure management tools.
How do I use get_service_scaling to change my service's capacity? +
You use get_service_scaling to read the current scaling configuration. Then, use update_service_scaling to manually adjust the instances. This lets you manage capacity directly through your agent.
What is the difference between list_projects and get_aws_account_details? +
list_projects retrieves a list of all projects owned by your team. get_aws_account_details provides specific, high-level details about the connected AWS account itself, regardless of the project.
Do I need to use create_job_execution for scheduled tasks? +
No, create_job_execution triggers a one-off job in a scheduler service. For persistent, scheduled tasks, you'll need to configure a dedicated scheduling resource outside of this 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.
Use it with your favorite AI tools
Connect this server to Cursor, Claude, VS Code, and more.
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