Application Auto Scaling MCP Configuration
The Application Auto Scaling MCP configuration provides a hosted JSON schema that connects AI assistants — including Claude Desktop, Cursor, Windsurf, and VS Code Copilot — to the Application Auto Scaling API via the Model Context Protocol. This configuration maps 10 API endpoints as callable tools, including DeleteScalingPolicy, DeleteScheduledAction, DeregisterScalableTarget, and more. No authentication credentials are needed — it works out of the box. The configuration is auto-generated from the Application Auto Scaling OpenAPI specification (v2016-02-06) and has a quality score of 46/99 (fair documentation coverage). Use the hosted URL below to auto-load this schema into any compatible MCP client.
Quick Specs Reference
Hosted Config URL
Use this hosted URL in any client that supports remote MCP schema auto-loading.
https://mcpbridge.org/config/amazonaws-com-application-autoscaling.jsonOne-Click Client Setup
Copy the configurations below to wire your local coding assistant directly.
Claude Desktop
claude_desktop_config.json{
"mcpServers": {
"amazonaws-com-application-autoscaling": {
"command": "npx",
"args": [
"-y",
"@mcp/amazonaws-com-application-autoscaling"
],
"env": {
"APPLICATION_AUTO_SCALING_API_KEY": "your_application_auto_scaling_api_key"
}
}
}
}Cursor & VS Code
MCP Server URL Setup{
"mcpServers": {
"amazonaws-com-application-autoscaling": {
"url": "https://mcpbridge.org/config/amazonaws-com-application-autoscaling.json"
}
}
}Raw Configuration JSON
For local command line wrappers or dynamic shell bindings.
{
"mcpServers": {
"amazonaws-com-application-autoscaling": {
"command": "npx",
"args": ["-y","@mcp/amazonaws-com-application-autoscaling"],
"env": {
"APPLICATION_AUTO_SCALING_API_KEY": "your_application_auto_scaling_api_key"
}
}
}
}Required Environment Keys
Substitute these secrets inside your configuration directory environment definitions.
APPLICATION_AUTO_SCALING_API_KEYyour_application_auto_scaling_api_key with your secret key credentialMapped Web APIs & Tools
The following routes will be exposed directly as protocol tools for the LLM.
/#X-Amz-Target=AnyScaleFrontendService.DeleteScalingPolicyDeleteScalingPolicy
/#X-Amz-Target=AnyScaleFrontendService.DeleteScheduledActionDeleteScheduledAction
/#X-Amz-Target=AnyScaleFrontendService.DeregisterScalableTargetDeregisterScalableTarget
/#X-Amz-Target=AnyScaleFrontendService.DescribeScalableTargetsDescribeScalableTargets
/#X-Amz-Target=AnyScaleFrontendService.DescribeScalingActivitiesDescribeScalingActivities
/#X-Amz-Target=AnyScaleFrontendService.DescribeScalingPoliciesDescribeScalingPolicies
/#X-Amz-Target=AnyScaleFrontendService.DescribeScheduledActionsDescribeScheduledActions
/#X-Amz-Target=AnyScaleFrontendService.ListTagsForResourceListTagsForResource
/#X-Amz-Target=AnyScaleFrontendService.PutScalingPolicyPutScalingPolicy
/#X-Amz-Target=AnyScaleFrontendService.PutScheduledActionPutScheduledAction
Similar Configurations
PostgreSQL (MCP)
Query and manage PostgreSQL databases directly from your AI agent. Read schemas, run queries, and manage data.
https://mcpbridge.org/config/postgres.jsonNotion API
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https://mcpbridge.org/config/notion-com.jsonAmazon CloudWatch Application Insights
Amazon CloudWatch Application Insights is a specialized observability service provided by Amazon Web Services (AWS) designed to simplify the monitoring and troubleshooting of applications, particularly those built on Microsoft IIS and .NET frameworks running on EC2 instances or within Elastic Beanstalk environments. Its core capability lies in automatically discovering application components, analyzing correlated metrics, logs, and traces to identify anomalies, and then surfacing actionable insights that pinpoint the root cause of common operational issues. By integrating seamlessly with other AWS services like CloudWatch, AWS X-Ray, and AWS Systems Manager, it provides a unified view of application health, reducing the mean time to resolution (MTTR) for performance degradations and errors. The typical use case spans enterprise environments managing distributed microservices or monolithic .NET applications, where teams need to proactively detect issues such as memory leaks, high CPU utilization, or specific application errors without manually configuring complex monitoring dashboards and alarms. When exposed as tools to an AI coding assistant through the Model Context Protocol (MCP), the Amazon CloudWatch Application Insights API becomes a powerful asset for intelligent development and operations automation. An AI agent, such as one integrated into Claude Desktop or Cursor, can leverage these endpoints to perform context-aware diagnostics and infrastructure adjustments directly within a developer's workflow. For instance, an AI could use the `DescribeApplication` and `DescribeComponent` tools to instantly fetch the current health status and configuration of a running application, providing a developer with a real-time summary during a debugging session. It could then utilize `DescribeComponentConfigurationRecommendation` to suggest optimal monitoring settings based on AWS best practices, or dynamically call `CreateLogPattern` to ingest new error logs identified during an AI-assisted code review, thereby automating the setup of precise observability for newly added application features. This transforms the AI from a passive code generator into an active participant in the application lifecycle, capable of bridging the gap between code deployment and operational monitoring. Practical workflows enabled by this MCP integration include dynamic infrastructure provisioning and reactive incident response. A developer could instruct the AI agent: "Analyze the error logs from the last deployment and, if a database connection timeout pattern is detected, create a new CloudWatch Application Insights component for the database tier and configure a log pattern to capture all related timeout events." The AI would execute the sequence by first querying logs, then using `CreateApplication` and `CreateComponent` to structure the monitoring, followed by `CreateLogPattern` to focus on the relevant data. Another scenario involves automated optimization: "Review the current monitoring configuration for my 'Checkout' service, compare it against the recommended settings, and apply the recommendations where they improve visibility into latency." Here, the AI would chain `DescribeComponentConfiguration`, `DescribeComponentConfigurationRecommendation`, and then update the configuration accordingly, automating a best-practice audit that would otherwise require manual console navigation and comparison. Despite the API endpoint listing showing "None" for authentication, all actions within Amazon CloudWatch Application Insights are governed by AWS Identity and Access Management (IAM) policies. Critical security best practices include enforcing the principle of least privilege by granting only the specific permissions required for the intended task, such as `cloudwatch:Describe*` for read-only access or `cloudwatch:Create*` and `cloudwatch:Delete*` for management functions. It is essential to use IAM roles with temporary credentials for any AI agent integration, never embedding long-term access keys in configuration files. Furthermore, network security should be maintained by ensuring the API calls originate from within a trusted VPC or are secured via AWS PrivateLink if applicable, and all access should be monitored and audited through AWS CloudTrail to maintain a compliance trail for any automated changes made by the AI assistant.
https://mcpbridge.org/config/amazonaws-com-application-insights.jsonAWS Cost Explorer Service
The AWS Cost Explorer API, provided by Amazon Web Services, serves as the programmatic backbone for the Cost Explorer service, a powerful tool designed to help organizations visualize, understand, and manage their AWS cloud spending. At its core, this API enables developers and financial operations (FinOps) teams to move beyond the web console and directly query their cost and usage data, unlocking the ability to build custom dashboards, automated reports, and sophisticated cost management applications. Its capabilities range from retrieving high-level aggregated data, such as monthly service costs or daily usage totals, to drilling down into granular, resource-level details, including the specific write operations of a DynamoDB table or the data transfer metrics of an EC2 instance. This granular access is critical for enterprises implementing showback/chargeback models, identifying optimization opportunities, and enforcing budget guardrails across complex, multi-account AWS environments. When exposed as a set of tools via the Model Context Protocol (MCP) to an AI coding assistant, the Cost Explorer API transforms from a query tool into a dynamic, conversational FinOps engine. An AI agent like Claude can leverage these tools to provide unprecedented, actionable cost intelligence directly within a developer's workflow. Instead of manually constructing API calls or navigating the console, a developer can simply ask the AI to "analyze our EC2 spending in the EU-West region over the last 90 days and identify any instances with consistently low utilization" or "compare our month-over-month costs for the RDS service and summarize the top three drivers of the increase." The AI can orchestrate calls to endpoints like `GetCostAndUsage` for data retrieval, then use anomaly-focused tools like `GetAnomalies` and `GetAnomalyMonitors` to proactively surface unexpected spending spikes, effectively acting as a real-time cost advisor that contextualizes financial data against technical usage patterns. Practical workflows enabled by this MCP integration are highly dynamic and task-oriented. A developer can instruct the AI agent to "create a new anomaly monitor to watch for daily cost variances greater than 15% in our production account" by invoking the `CreateAnomalyMonitor` tool. The AI can then use the `DescribeCostCategoryDefinition` tool to understand the existing cost allocation tags and, based on a developer's rule, automatically create a new `CostCategoryDefinition` to segment costs by team or project. For ongoing management, a developer could command, "Generate a daily summary of all anomalies detected in the last 24 hours and draft an email report," prompting the AI to chain calls to `GetAnomalies` and format the results. This allows for the automation of reporting, the proactive management of cost controls, and the acceleration of optimization initiatives by embedding expert cost analysis directly into the development and DevOps toolchain. It is critical to note that while the service name in the API target header is "AWSInsightsIndexService," authentication is not "None" in practice. The API is secured through standard AWS Identity and Access Management (IAM) authentication mechanisms. All requests must be signed using the AWS Signature Version 4 process, which requires valid IAM credentials (access key and secret access key) from an authorized IAM user or role. Security best practices are paramount: developers should adhere strictly to the principle of least privilege, creating IAM policies that grant only the specific Cost Explorer API actions required for a tool's function (e.g., `ce:GetCostAndUsage`, `ce:CreateAnomalyMonitor`) and scoping them to specific resources or periods where possible. Credentials should never be hardcoded; instead, environment variables, AWS roles for services (like Lambda), or the default credential provider chain should be used. Furthermore, API calls should be monitored via AWS CloudTrail, and any MCP server integration must ensure secure handling and storage of any temporary credentials used to interact with this sensitive financial data.
https://mcpbridge.org/config/amazonaws-com-ce.json