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DatabasesQuality Score: 46

Amazon CloudWatch Application Insights MCP Configuration

The Amazon CloudWatch Application Insights MCP configuration provides a hosted JSON schema that connects AI assistants — including Claude Desktop, Cursor, Windsurf, and VS Code Copilot — to the Amazon CloudWatch Application Insights API via the Model Context Protocol. This configuration maps 10 API endpoints as callable tools, including CreateApplication, CreateComponent, CreateLogPattern, and more. No authentication credentials are needed — it works out of the box. The configuration is auto-generated from the Amazon CloudWatch Application Insights OpenAPI specification (v2018-11-25) 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

AuthenticationNo Auth Required
Available Endpoints10 tools mapped
Integration Modeauto Generation

Hosted Config URL

Use this hosted URL in any client that supports remote MCP schema auto-loading.

https://mcpbridge.org/config/amazonaws-com-application-insights.json

One-Click Client Setup

Copy the configurations below to wire your local coding assistant directly.

Claude Desktop

claude_desktop_config.json
{
  "mcpServers": {
    "amazonaws-com-application-insights": {
      "command": "npx",
      "args": [
        "-y",
        "@mcp/amazonaws-com-application-insights"
      ],
      "env": {
        "AMAZON_CLOUDWATCH_APPLICATION_INSIGHTS_API_KEY": "your_amazon_cloudwatch_application_insights_api_key"
      }
    }
  }
}

Cursor & VS Code

MCP Server URL Setup
{
  "mcpServers": {
    "amazonaws-com-application-insights": {
      "url": "https://mcpbridge.org/config/amazonaws-com-application-insights.json"
    }
  }
}

Raw Configuration JSON

For local command line wrappers or dynamic shell bindings.

{
  "mcpServers": {
    "amazonaws-com-application-insights": {
      "command": "npx",
      "args": ["-y","@mcp/amazonaws-com-application-insights"],
      "env": {
      "AMAZON_CLOUDWATCH_APPLICATION_INSIGHTS_API_KEY": "your_amazon_cloudwatch_application_insights_api_key"
}
    }
  }
}

Required Environment Keys

Substitute these secrets inside your configuration directory environment definitions.

AMAZON_CLOUDWATCH_APPLICATION_INSIGHTS_API_KEY
Replace your_amazon_cloudwatch_application_insights_api_key with your secret key credential

Mapped Web APIs & Tools

The following routes will be exposed directly as protocol tools for the LLM.

POST/#X-Amz-Target=EC2WindowsBarleyService.CreateApplication

CreateApplication

POST/#X-Amz-Target=EC2WindowsBarleyService.CreateComponent

CreateComponent

POST/#X-Amz-Target=EC2WindowsBarleyService.CreateLogPattern

CreateLogPattern

POST/#X-Amz-Target=EC2WindowsBarleyService.DeleteApplication

DeleteApplication

POST/#X-Amz-Target=EC2WindowsBarleyService.DeleteComponent

DeleteComponent

POST/#X-Amz-Target=EC2WindowsBarleyService.DeleteLogPattern

DeleteLogPattern

POST/#X-Amz-Target=EC2WindowsBarleyService.DescribeApplication

DescribeApplication

POST/#X-Amz-Target=EC2WindowsBarleyService.DescribeComponent

DescribeComponent

POST/#X-Amz-Target=EC2WindowsBarleyService.DescribeComponentConfiguration

DescribeComponentConfiguration

POST/#X-Amz-Target=EC2WindowsBarleyService.DescribeComponentConfigurationRecommendation

DescribeComponentConfigurationRecommendation

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.json

Notion API

The Notion API is a comprehensive RESTful interface provided by Notion, the popular all-in-one workspace platform, enabling programmatic interaction with its rich set of collaborative objects. It grants developers and automated systems the ability to read, create, update, and manage core Notion entities such as blocks (the fundamental building blocks of content like text, lists, and media), databases (structured tables with properties), pages (containers for content and databases), and comments. Typical use cases span enterprise and consumer scenarios, including automating team workflows, syncing data between Notion and other business systems (like CRM, project management, or analytics tools), building custom dashboards, generating dynamic reports, and enhancing content collaboration through programmatic updates. Organizations leverage this API to break down data silos, enforce process automation, and create tailored integrations that extend Notion's native capabilities for specific departmental or cross-functional needs. When these specific Notion API endpoints are exposed as tools via the Model Context Protocol (MCP) for an AI coding assistant, they transform the assistant from a passive code generator into an active, context-aware collaborator that can directly interact with a team's live knowledge base and operational data. The value lies in dynamic, real-time data access and manipulation within the development workflow. Instead of the developer manually copying data, checking status, or updating records, the AI agent can perform these actions conversationally. For instance, an MCP server implementing these endpoints allows the AI to query a project database to fetch current sprint tasks (using POST /v1/databases/{id}/query), read the details of a specific feature page (GET /v1/pages/{id}), or even update the status of a completed task by patching its block content (PATCH /v1/blocks/{id}). This creates a powerful feedback loop where the AI is grounded in the actual, up-to-date project context, leading to more accurate code suggestions, documentation that reflects current system states, and automated updates that maintain consistency across development and project management tools. Practical workflow examples demonstrate significant productivity gains. A developer can instruct the AI agent: "Query our Notion database of API specs, find the entry for the 'User Auth' endpoint, and use its latest property values to generate a complete OpenAPI 3.0 YAML definition in the current file." The AI would use the database query and page retrieval tools to fetch the live data and produce code. In another scenario, a developer could say, "After we finish refactoring this service, please update the 'Progress' property on our project tracking page for 'Backend Refactor' to 95% and add a comment with the key changes made." The AI would use the PATCH endpoints on the page and blocks to update the database property and append a new comment block, automating routine project management bookkeeping. For incident response, one could command, "Create a new page under our 'Incident Log' database for today's outage, pre-populate the 'Status' and 'Severity' properties, and add an initial block with a summary of the service affected," enabling rapid, structured documentation directly from the chat interface. Critical authentication and security configuration are paramount when setting up an MCP server for the Notion API. While the provided endpoint list omits authentication details, the official Notion API mandates the use of either a Notion Integration (internal integration) or OAuth for accessing a workspace. Developers must first create a Notion Integration via the developer portal to obtain an Internal Integration Token (a secret API key). This token must be securely stored and injected into the MCP server's environment, never exposed in client-side code or version control. The principle of least privilege is essential: the integration's capabilities should be scoped precisely within the Notion workspace, granting access only to the specific databases and pages required for the intended automation, and using read-only permissions where possible. Furthermore, when sharing the MCP server configuration with AI tools, the developer must ensure that the tool's access to the server is itself secured and that all API requests are proxied through a trusted backend to avoid direct exposure of the Notion token to the AI model's runtime environment. Regular review of integration permissions and audit logs is a necessary best practice.

https://mcpbridge.org/config/notion-com.json

Application Auto Scaling

The Application Auto Scaling API, provided by Amazon Web Services (AWS), is a robust service designed to automate the scaling of computing resources for a wide array of AWS services, ensuring optimal performance, availability, and cost efficiency. Its core capability is to define policies that automatically adjust the provisioned capacity of supported resources in response to changing demand, as measured by CloudWatch metrics or predefined schedules. Beyond the initially listed resources, it supports scaling for Amazon DynamoDB tables and global secondary indexes, Amazon ECS services running on Fargate or EC2, Amazon ElastiCache replication groups, Amazon Neptune clusters, Amazon SageMaker endpoint variants, and custom resources via the AWS Lambda-backed scalable target. This makes it a central tool for architects and DevOps engineers in building resilient, self-optimizing cloud architectures. Typical enterprise use cases include dynamically adjusting the number of Aurora read replicas to handle database query load spikes, scaling ECS task counts during peak traffic for a microservices application, or optimizing costs by scaling down SageMaker inference endpoints during off-hours. When exposed as tools to an AI coding assistant via the Model Context Protocol (MCP), this API gains significant contextual power. An AI agent, such as Claude or a specialized coding assistant, can directly inspect, reason about, and manipulate an application's scaling configuration in real-time. The value lies in transforming static infrastructure code or manual console operations into dynamic, conversational management. The AI can query the current scaling state (e.g., "describe all registered scalable targets and their current capacity"), analyze scaling activity logs to diagnose performance issues (e.g., "what scaling activities occurred on my ECS service in the past hour?"), or even propose and validate configuration changes (e.g., "draft a scaling policy to maintain average CPU at 40% for my Aurora cluster"). This creates a powerful feedback loop where the AI assistant can act as an expert collaborator, helping developers quickly understand, debug, and evolve their auto-scaling strategies. Practical workflows enabled by this MCP server are numerous and impactful. A developer could instruct the AI: "List all my scalable targets for Amazon Aurora and describe their current scaling policies to check for misconfigurations." The agent would execute the corresponding DescribeScalableTargets and DescribeScalingPolicies calls, then summarize the findings, perhaps flagging a policy with an aggressive cooldown period. Another dynamic task could be: "For my ECS service named 'checkout-service,' create a scheduled action to scale out to 10 tasks every weekday at 9 AM EST and scale in to 3 tasks at 5 PM EST." The AI would use PutScheduledAction to implement this, verifying the time zone and parameters. Furthermore, an AI agent could be tasked with cleanup and optimization: "Identify any scaling policies for DynamoDB tables that have not triggered a scaling activity in 30 days and suggest whether to keep or delete them, then remove the unused ones." This involves querying DescribeScalingActivities and then calling DeleteScalingPolicy based on the analysis, automating routine maintenance. Critical for implementation are authentication and security, as the API actions perform privileged infrastructure changes. While the endpoint list notes "None" for authentication, in a real-world deployment, this API must be invoked with temporary AWS credentials obtained through an IAM role or user with precisely scoped permissions. Developers must adhere to the principle of least privilege when creating the policy document for the AI assistant's execution role. Permissions should be narrowly tailored to only the necessary actions and resource ARNs. For example, a role might allow `application-autoscaling:DescribeScalableTargets` and `application-autoscaling:PutScalingPolicy` only for a specific service namespace and resource ID, preventing unintended modifications. All API calls should be logged via AWS CloudTrail for auditability. It is also essential to ensure that the MCP server configuration securely manages any AWS credentials or role assumptions, preferably through environment variables or a secure secret manager, and never hardcodes them.

https://mcpbridge.org/config/amazonaws-com-application-autoscaling.json

AWS 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