Enhancing Azure Kubernetes Service AKS Monitoring With AppLens Resource Health And Azure Advisor

Jul 10, 2025 by Jeany 97 views

Enhancing AKS Monitoring and Diagnostics with AppLens, Resource Health, and Azure Advisor

Microsoft Azure Kubernetes Service (AKS) has become a cornerstone for deploying and managing containerized applications. To ensure the health, performance, and security of these applications, robust diagnostic and advisory tools are essential. This article delves into the integration of three powerful Azure services—AppLens, Resource Health, and Azure Advisor—into AKS monitoring and diagnostic processes. By leveraging these tools, AKS administrators and developers can proactively identify and resolve issues, optimize resource utilization, and maintain a secure and reliable environment.

Implementation Request: Integrating Diagnostic and Advisory Tools into AKS-MCP

This article addresses an implementation request to enhance the diagnostic capabilities of Azure Kubernetes Service (AKS) by integrating AppLens, Resource Health, and Azure Advisor. These tools provide critical insights into the health, performance, and optimization of AKS clusters. The goal is to seamlessly incorporate these services into the AKS Management Control Plane (MCP) server, offering a unified interface for monitoring and troubleshooting.

1. AppLens Detector Integration

AppLens is a powerful diagnostic tool that helps identify and troubleshoot issues within Azure services. Integrating AppLens into AKS-MCP allows for proactive detection of problems and provides actionable recommendations.

Tool: `invoke_applens_detector`

Purpose: To call and invoke AppLens detectors for AKS clusters, this tool is critical for real-time diagnostics. It allows users to run specific detectors or list available ones, providing insights into cluster health and potential issues. This proactive approach to diagnostics ensures that problems are identified and addressed before they escalate, minimizing downtime and improving overall cluster performance.

Parameters:

cluster_resource_id (required): Full Azure resource ID of the AKS cluster. This parameter is essential for identifying the specific cluster to be analyzed. The resource ID acts as a unique identifier, ensuring that the diagnostic tools are applied to the correct resource. Proper use of this parameter guarantees accurate and targeted diagnostics, preventing any confusion or misapplication of resources.
detector_name (optional): Specific detector to run; if not provided, list available detectors. The flexibility to specify a detector allows users to focus on particular aspects of cluster health. When no detector is specified, the tool provides a list of available detectors, enabling users to explore the diagnostic options available to them. This adaptability ensures that the tool can be used for both targeted investigations and broader assessments of cluster health.
time_range (optional): Time range for analysis (e.g., "24h", "7d", "30d"). By defining a time range, users can analyze historical data and identify trends or recurring issues. This capability is invaluable for understanding the evolution of cluster health over time. The ability to specify different time ranges, such as 24 hours, 7 days, or 30 days, allows for both short-term and long-term analysis, providing a comprehensive view of cluster performance.

Expected Outputs:

List of available detectors with descriptions. Providing a list of detectors with descriptions helps users understand the diagnostic capabilities available to them. This ensures that users can make informed decisions about which detectors to run, optimizing their troubleshooting efforts. The descriptions offer context and guidance, making the tool more accessible and user-friendly.
Detector execution results with findings and recommendations. The core output of the tool is the execution results, which include findings and recommendations. These results provide actionable insights, helping users understand the nature of any issues and the steps required to resolve them. Clear and concise findings are essential for effective problem-solving, enabling users to quickly address and mitigate issues.
Severity levels and impact assessment. Understanding the severity of an issue and its potential impact is critical for prioritizing remediation efforts. The tool provides severity levels and impact assessments, allowing users to focus on the most critical problems first. This prioritization ensures that resources are allocated efficiently, and the most pressing issues are addressed promptly.
Actionable remediation steps. The tool goes beyond identifying problems by providing actionable remediation steps. These steps guide users through the process of resolving issues, reducing the time and effort required for troubleshooting. Clear, step-by-step instructions ensure that users can confidently address problems, improving the overall reliability of the cluster.

Implementation Requirements:

Use Azure Management SDK for AppLens API calls. The Azure Management SDK provides the necessary tools and interfaces for interacting with the AppLens API. Using the SDK ensures that the integration is robust and adheres to best practices. The SDK also handles many of the complexities of API communication, simplifying the development process.
Handle authentication via Azure credential chain. Proper authentication is essential for secure access to Azure services. The tool uses the Azure credential chain, which supports various authentication methods, ensuring that access is secure and compliant with organizational policies. This flexible authentication mechanism allows the tool to be used in different environments without requiring code changes.
Support both listing detectors and executing specific detectors. The tool supports both listing available detectors and executing specific ones, providing flexibility in how diagnostics are performed. This dual functionality allows users to explore the diagnostic landscape and then focus on specific areas of concern. The ability to list detectors is valuable for discovery, while the ability to execute specific detectors enables targeted troubleshooting.
Parse and format detector results for readability. The raw results from the AppLens API can be complex and difficult to interpret. The tool parses and formats these results, making them more readable and understandable for users. Clear and concise presentation of results is essential for effective communication of diagnostic findings.
Handle rate limiting and API quotas. Azure APIs have rate limits and quotas to prevent abuse and ensure service availability. The tool implements mechanisms to handle these limits gracefully, such as retries and backoff strategies. This ensures that the tool can function reliably, even under heavy load or when dealing with large clusters. Proper handling of rate limits is crucial for maintaining the stability of diagnostic operations.

Tool: `list_applens_detectors`

Purpose: To list all available AppLens detectors for a cluster. This tool is essential for discovering the diagnostic capabilities of AppLens within the context of an AKS cluster. By providing a comprehensive list of detectors, it enables users to understand the full range of available diagnostic options, facilitating proactive and informed troubleshooting efforts. The tool serves as a valuable resource for both new and experienced users, helping them leverage AppLens effectively.

Parameters:

cluster_resource_id (required): Full Azure resource ID of the AKS cluster. The cluster resource ID is a critical parameter, as it uniquely identifies the AKS cluster for which the available detectors need to be listed. This parameter ensures that the tool operates within the correct scope, preventing accidental or erroneous actions on other clusters. Proper specification of the cluster resource ID is fundamental to the tool's functionality.
category (optional): Filter by detector category (performance, security, reliability). The ability to filter detectors by category adds a layer of precision to the diagnostic process. By specifying categories such as performance, security, or reliability, users can narrow down the list to the most relevant detectors for their specific needs. This filtering capability enhances efficiency, allowing users to focus on the diagnostic aspects that are most pertinent to their current concerns. It helps in streamlining the troubleshooting process and improving overall diagnostic accuracy.

Expected Outputs:

Comprehensive list of available detectors. The primary output of this tool is a comprehensive list of AppLens detectors applicable to the specified AKS cluster. This list serves as a directory of diagnostic tools, allowing users to explore the full scope of AppLens' capabilities. The comprehensive nature of the list ensures that no potential diagnostic resource is overlooked, promoting thorough and effective troubleshooting.
Detector categories and descriptions. In addition to the detector names, the tool provides categories and descriptions for each detector. This contextual information is crucial for users to understand the purpose and applicability of each detector. Descriptions offer a brief overview of the detector's function, while categories help organize the detectors by diagnostic focus (e.g., performance, security). This detail aids users in selecting the most appropriate detectors for their diagnostic goals, improving the efficiency of their troubleshooting efforts.
Execution time estimates. Providing execution time estimates for each detector is a valuable feature that helps users plan their diagnostic activities. Knowing the approximate time it will take to run a detector allows users to prioritize tasks and allocate resources effectively. This feature is particularly useful in scenarios where time is of the essence, enabling users to make informed decisions about which detectors to run and when.
Prerequisites for each detector. Some detectors may have specific prerequisites or dependencies that must be met before they can be executed successfully. The tool lists these prerequisites, ensuring that users are aware of any required conditions. This feature helps prevent errors and ensures that detectors are run under the appropriate circumstances, leading to more accurate and reliable diagnostic results. By highlighting prerequisites, the tool enhances the usability and effectiveness of AppLens diagnostics.

2. Resource Health Event Tools

Resource Health provides insights into the health of Azure resources, including AKS clusters. By integrating Resource Health events, AKS-MCP can provide real-time and historical health information.

Tool: `get_resource_health_status`

Purpose: To access current resource health status for AKS clusters. This tool is critical for providing an up-to-date snapshot of the health of AKS clusters, enabling administrators to quickly identify any ongoing issues. By accessing the current health status, users can proactively address problems, ensuring the reliability and performance of their Kubernetes deployments. The tool's focus on real-time health information makes it an essential component of any monitoring and diagnostic strategy.

Parameters:

resource_ids (required): Array of Azure resource IDs (supports multiple clusters). The ability to handle an array of resource IDs is a key feature, allowing users to monitor the health of multiple AKS clusters simultaneously. This bulk monitoring capability is particularly valuable in environments with numerous clusters, streamlining the diagnostic process and providing a comprehensive overview of the entire infrastructure. Supporting multiple clusters enhances the tool's scalability and efficiency.
include_history (optional): Boolean to include recent health events. The option to include recent health events provides additional context to the current health status. By incorporating historical data, users can better understand the nature of any issues, identify patterns, and track the effectiveness of remediation efforts. This historical perspective is crucial for long-term cluster management and maintenance.

Expected Outputs:

Current health status (Available, Unavailable, Degraded, Unknown). The primary output of the tool is the current health status of the AKS cluster, categorized into one of several states: Available, Unavailable, Degraded, or Unknown. This clear categorization allows users to quickly assess the overall health of the cluster and prioritize their actions accordingly. The health status serves as a high-level indicator, guiding users to focus on clusters that require immediate attention.
Health summary with key metrics. In addition to the overall health status, the tool provides a health summary that includes key metrics. These metrics offer a more granular view of the cluster's health, highlighting specific areas of concern. By presenting key performance indicators (KPIs), the tool helps users understand the underlying factors contributing to the cluster's health status.
Active health issues and their impact. Identifying active health issues is a critical function of the tool. It provides details on any ongoing problems, helping users understand the specific nature of the issues affecting the cluster. Furthermore, the tool assesses the impact of these issues, allowing users to prioritize remediation efforts based on the potential consequences. This focus on active issues and their impact ensures that users address the most critical problems first.
Recommended actions for degraded health. The tool goes beyond simply identifying issues by providing recommended actions for degraded health. These recommendations guide users through the steps needed to resolve problems and restore the cluster to a healthy state. Actionable advice is invaluable in reducing the time and effort required for troubleshooting, enabling users to address issues efficiently and effectively. By providing clear remediation steps, the tool enhances the overall manageability of AKS clusters.

Tool: `get_resource_health_events`

Purpose: To retrieve historical resource health events. Accessing historical health events is crucial for understanding the evolution of cluster health over time. This tool enables users to identify trends, patterns, and recurring issues, facilitating proactive management and maintenance. By analyzing historical data, administrators can make informed decisions about resource allocation, configuration adjustments, and other optimization strategies. The tool's ability to retrieve historical health events is essential for long-term cluster health management.

Parameters:

resource_id (required): Azure resource ID of the AKS cluster. The resource ID is a fundamental parameter, uniquely identifying the AKS cluster for which historical health events are to be retrieved. This ensures that the tool operates within the correct scope, preventing accidental or erroneous actions on other clusters. Proper specification of the resource ID is essential for the tool's functionality.
start_time (optional): Start time for historical query (ISO 8601 format). The ability to specify a start time allows users to define the beginning of the historical period they wish to analyze. This is crucial for focusing on specific timeframes, such as periods of known issues or critical events. The ISO 8601 format ensures standardized time representation, avoiding ambiguity and ensuring compatibility across systems. By using a start time, users can tailor their historical queries to meet their specific diagnostic needs.
end_time (optional): End time for historical query (ISO 8601 format). Complementary to the start time, the end time parameter defines the end of the historical period to be queried. Together, the start and end times delineate a specific window of time for analysis. This precision in time specification enables users to isolate and examine particular periods of interest, enhancing the accuracy and relevance of their diagnostic efforts. The standardized ISO 8601 format ensures consistency and clarity in time representation.
health_status_filter (optional): Filter by health status types. The option to filter by health status types (e.g., Available, Unavailable, Degraded) adds a layer of refinement to the query. Users can narrow down the results to focus on events of a particular health status, such as identifying all instances of a cluster being in a Degraded state. This filtering capability is invaluable for targeted analysis, helping users quickly identify and understand the factors contributing to specific health issues.

Expected Outputs:

Historical health events with timestamps. The primary output of this tool is a list of historical health events, each with a corresponding timestamp. Timestamps are crucial for understanding the chronological sequence of events, allowing users to track the evolution of cluster health over time. This chronological perspective is essential for identifying patterns, diagnosing root causes, and assessing the impact of remediation efforts. The combination of events and timestamps provides a comprehensive view of historical cluster health.
Event duration and impact scope. For each health event, the tool provides information on its duration and impact scope. The duration indicates how long the event lasted, while the impact scope describes the extent to which the event affected the cluster and its resources. Understanding these parameters is critical for assessing the severity of each event and prioritizing remediation efforts accordingly. Duration and impact scope help users differentiate between transient issues and more significant, persistent problems.
Root cause analysis when available. In many cases, the tool provides a root cause analysis for historical health events. This analysis helps users understand the underlying causes of issues, enabling them to implement preventive measures and avoid recurrence. Identifying root causes is a key step in long-term cluster management, ensuring that problems are not only resolved but also prevented in the future. The inclusion of root cause analysis enhances the tool's diagnostic capabilities, making it a valuable resource for proactive cluster maintenance.
Resolution status and time to resolution. The tool also tracks the resolution status of health events, indicating whether an event has been resolved and, if so, how long it took to resolve. This information is essential for measuring the effectiveness of remediation efforts and identifying areas for improvement. The time to resolution is a key metric for assessing the efficiency of incident response processes, while the resolution status ensures that all issues are tracked to completion. By providing this data, the tool supports continuous improvement in cluster management practices.

Implementation Requirements:

Use Azure Resource Health REST API. Interacting with the Azure Resource Health REST API is a foundational requirement for this tool. The REST API provides the necessary interfaces for querying and retrieving resource health information. Using the REST API ensures that the tool adheres to Azure standards and best practices, facilitating seamless integration with the Azure ecosystem. This approach also allows for flexibility in data retrieval and manipulation, enabling the tool to meet a wide range of diagnostic needs.
Support filtering by time range and health status. The ability to filter health events by time range and health status is crucial for targeted analysis. This allows users to focus on specific periods of interest and particular types of health issues, improving the efficiency of their diagnostic efforts. Filtering capabilities are essential for handling large datasets and extracting relevant information quickly. By supporting these filters, the tool enhances the usability and effectiveness of historical health event retrieval.
Handle large datasets with pagination. Historical health event datasets can be quite large, particularly for long-running clusters. The tool must implement pagination to handle these large datasets efficiently, retrieving data in manageable chunks. Pagination ensures that the tool can scale to meet the demands of large clusters without performance degradation. This approach is crucial for maintaining responsiveness and preventing timeouts when querying extensive historical data.
Provide clear event categorization and severity. Clear categorization and severity assessment are essential for users to understand the nature and impact of health events. The tool should categorize events into meaningful groups (e.g., connectivity, performance, security) and assign severity levels (e.g., Critical, Warning, Informational) to indicate the potential impact of each event. This structured approach helps users prioritize their responses and focus on the most critical issues first. By providing clear event categorization and severity, the tool enhances the overall clarity and actionability of health event data.

3. Azure Advisor Tools

Azure Advisor provides recommendations for optimizing Azure resources. By integrating Azure Advisor, AKS-MCP can offer insights into cost optimization, performance improvements, security enhancements, and reliability.

Tool: `get_azure_advisor_recommendations`

Purpose: To access active Azure Advisor recommendations. This tool is vital for providing actionable insights to optimize AKS clusters across various dimensions, including cost, performance, security, and reliability. By accessing active recommendations, users can proactively identify areas for improvement and implement best practices. The tool serves as a valuable resource for continuous optimization, helping users maintain a well-managed and efficient Kubernetes environment.

Parameters:

subscription_id (required): Azure subscription ID. The subscription ID is a fundamental parameter, as it identifies the Azure subscription for which Advisor recommendations should be retrieved. This parameter ensures that the tool operates within the correct scope, preventing accidental or erroneous actions on other subscriptions. Proper specification of the subscription ID is essential for the tool's functionality.
resource_group (optional): Filter by specific resource group. The ability to filter by resource group adds a layer of precision to the recommendation retrieval process. Users can narrow down the results to focus on recommendations relevant to a specific resource group, streamlining their optimization efforts. This filtering capability is particularly useful in environments with multiple resource groups, allowing users to manage recommendations in a targeted manner.
category (optional): Filter by recommendation category (Cost, Performance, Security, Reliability). Filtering by category enables users to focus on specific areas of optimization, such as cost, performance, security, or reliability. This categorization helps users prioritize their actions based on their specific goals and requirements. By specifying a category, users can quickly identify recommendations that align with their current objectives, improving the efficiency of their optimization efforts.
severity (optional): Filter by severity level (High, Medium, Low). The option to filter by severity level (High, Medium, Low) is crucial for prioritizing recommendations based on their potential impact. High-severity recommendations typically address critical issues that should be addressed immediately, while lower-severity recommendations may be addressed in a less urgent manner. This filtering capability helps users focus on the most pressing optimization opportunities, ensuring that resources are allocated effectively.

Expected Outputs:

List of active recommendations with descriptions. The primary output of this tool is a list of active Azure Advisor recommendations, each with a detailed description. This list serves as a roadmap for optimization, highlighting areas where improvements can be made. The descriptions provide context and guidance, helping users understand the nature of each recommendation and its potential benefits. The comprehensiveness of this list ensures that no optimization opportunity is overlooked.
Severity levels and priority ranking. For each recommendation, the tool provides a severity level (High, Medium, Low) and a priority ranking. These indicators help users prioritize their actions, focusing on the most critical recommendations first. Severity levels reflect the potential impact of the issue being addressed, while priority ranking provides a relative measure of importance. This combined information enables users to make informed decisions about which recommendations to implement and when.
Estimated impact and potential savings. A key feature of the tool is its ability to estimate the impact and potential savings associated with each recommendation. This includes both quantitative metrics (e.g., cost savings, performance improvements) and qualitative assessments (e.g., improved security posture, enhanced reliability). By quantifying the benefits of each recommendation, the tool helps users justify their optimization efforts and demonstrate the value of Azure Advisor. This financial and operational impact assessment is crucial for securing buy-in and driving adoption of recommendations.
Implementation guidance and steps. The tool goes beyond simply identifying recommendations by providing implementation guidance and steps. This includes detailed instructions on how to implement each recommendation, reducing the time and effort required for optimization. Actionable guidance is essential for driving adoption, ensuring that recommendations are not only identified but also implemented effectively. By providing clear implementation steps, the tool enhances the overall usability and value of Azure Advisor.

Tool: `get_advisor_recommendation_details`

Purpose: To get detailed information about specific recommendations. This tool is crucial for users who require a deeper understanding of a particular Azure Advisor recommendation before taking action. By providing detailed information, the tool enables users to make informed decisions about implementation, ensuring that they fully understand the potential benefits and risks. The detailed insights facilitate more effective and targeted optimization efforts.

Parameters:

recommendation_id (required): Unique identifier for the recommendation. The recommendation ID is a fundamental parameter, uniquely identifying the specific recommendation for which details are to be retrieved. This ensures that the tool operates within the correct scope, preventing confusion or misapplication of information. Proper specification of the recommendation ID is essential for the tool's functionality.
include_implementation_status (optional): Include tracking of implementation progress. The option to include tracking of implementation progress is a valuable feature for managing ongoing optimization efforts. By monitoring the status of implementation, users can ensure that recommendations are being addressed in a timely and effective manner. This tracking capability facilitates project management and provides visibility into the overall progress of optimization initiatives. The inclusion of implementation status supports a proactive and data-driven approach to recommendation management.

Expected Outputs:

Detailed recommendation description. The primary output of this tool is a detailed description of the recommendation. This description provides a comprehensive overview of the issue being addressed, the proposed solution, and the potential benefits of implementation. A detailed understanding is essential for users to make informed decisions and effectively implement the recommendation. The description serves as the foundation for further analysis and action.
Technical implementation steps. In addition to the description, the tool provides detailed technical implementation steps. These steps guide users through the process of implementing the recommendation, ensuring that they have the necessary information to take action. Clear and actionable steps are crucial for driving adoption and ensuring successful implementation. The technical steps bridge the gap between understanding the recommendation and putting it into practice.
Risk assessment and impact analysis. A key component of the detailed information is a risk assessment and impact analysis. This analysis helps users understand the potential risks associated with implementing the recommendation, as well as the expected impact on the system. A thorough risk assessment ensures that users are aware of any potential downsides and can take appropriate mitigation measures. The impact analysis provides a holistic view of the consequences, enabling users to make informed decisions about implementation.
Cost-benefit analysis where applicable. For recommendations with financial implications, the tool provides a cost-benefit analysis. This analysis compares the costs of implementing the recommendation with the potential savings or benefits. A cost-benefit analysis is crucial for justifying optimization efforts and securing buy-in from stakeholders. By quantifying the financial impact, the tool supports data-driven decision-making and helps users prioritize recommendations based on their economic value.

Technical Implementation Guidelines

Authentication and Authorization

Proper authentication and authorization are critical for securing access to Azure resources. The implementation should use the Azure SDK default credential chain to handle authentication, ensuring that access is secure and compliant with organizational policies.

// Use Azure SDK default credential chain
credential, err := azidentity.NewDefaultAzureCredential(nil)
if err != nil {
    return fmt.Errorf("failed to create Azure credential: %w", err)
}

Error Handling

Comprehensive error handling is essential for robust applications. The implementation should include error handling for API failures, permission issues, service outages, and rate limiting. Meaningful error messages should be provided to assist with troubleshooting.

Implement comprehensive error handling for API failures
Provide meaningful error messages for permission issues
Handle service outages and rate limiting gracefully
Log diagnostic information for troubleshooting

Data Processing

Efficient data processing is necessary for handling large datasets and providing timely insights. The implementation should parse and format API responses for readability, implement caching for frequently accessed data, and support both real-time and historical data queries.

Parse and format API responses for readability
Implement caching for frequently accessed data
Support real-time and historical data queries
Provide data aggregation and correlation capabilities

Integration with MCP Framework

Seamless integration with the existing MCP framework is crucial for maintaining consistency and compatibility. The implementation should follow existing MCP tool patterns, integrate with current authentication and configuration systems, support all access levels, and maintain consistent error handling and logging.

Follow existing MCP tool patterns in the codebase
Integrate with current authentication and configuration systems
Support all access levels (readonly, readwrite, admin)
Maintain consistent error handling and logging

Code Structure Requirements

File Organization

A well-organized file structure is essential for maintainability and scalability. The implementation should follow a modular approach, with separate packages for AppLens, Resource Health, and Azure Advisor.

internal/azure/
├── applens/
│   ├── client.go          # AppLens API client
│   ├── detectors.go       # Detector management
│   └── types.go           # AppLens data types
├── resourcehealth/
│   ├── client.go          # Resource Health API client
│   ├── events.go          # Health event handling
│   └── types.go           # Resource Health data types
└── advisor/
    ├── client.go          # Azure Advisor API client
    ├── recommendations.go # Recommendation handling
    └── types.go           # Advisor data types

Tool Registration

Each tool should be registered in the MCP server to make it accessible. This involves adding the tool to the server's tool registry and associating it with a handler function.

// Add to internal/server/server.go
func (s *Server) registerDiagnosticTools() {
    s.registerTool("invoke_applens_detector", s.handleAppLensDetector)
    s.registerTool("list_applens_detectors", s.handleListAppLensDetectors)
    s.registerTool("get_resource_health_status", s.handleResourceHealthStatus)
    s.registerTool("get_resource_health_events", s.handleResourceHealthEvents)
    s.registerTool("get_azure_advisor_recommendations", s.handleAdvisorRecommendations)
    s.registerTool("get_advisor_recommendation_details", s.handleAdvisorDetails)
}

Configuration Support

Configuration options should be provided for API endpoints, timeouts, authentication methods, default time ranges, and filters. This allows administrators to customize the behavior of the tools to meet their specific needs.

Add configuration options for API endpoints and timeouts
Support custom authentication methods
Allow configuration of default time ranges and filters
Enable/disable specific diagnostic tools based on access level

Testing Requirements

Unit Tests

Unit tests should be written for each tool to ensure its functionality and reliability. This includes testing with various input parameters, mocking Azure API responses, validating error handling, and testing authentication and authorization scenarios.

Test each tool with various input parameters
Mock Azure API responses for consistent testing
Validate error handling and edge cases
Test authentication and authorization scenarios

Integration Tests

Integration tests should be performed with real Azure resources in a test environment. This includes validating API integration, data parsing, performance with large datasets, and cross-tool data correlation.

Test with real Azure resources (in test environment)
Validate API integration and data parsing
Test performance with large datasets
Verify cross-tool data correlation

Example Test Cases

Example test cases should be provided to demonstrate how to test the tools. These examples should cover various scenarios and input parameters.

func TestAppLensDetectorInvocation(t *testing.T) {
    // Test invoking specific detector
    // Test listing available detectors
    // Test error handling for invalid clusters
}

func TestResourceHealthEvents(t *testing.T) {
    // Test current health status retrieval
    // Test historical event queries
    // Test filtering and pagination
}

func TestAzureAdvisorRecommendations(t *testing.T) {
    // Test recommendation retrieval
    // Test filtering by category and severity
    // Test detailed recommendation access
}

Documentation Requirements

Tool Documentation

Comprehensive documentation should be provided for each tool, including descriptions, parameter specifications, examples, expected outputs, and troubleshooting guides.

Provide comprehensive tool descriptions
Include parameter specifications and examples
Document expected outputs and formats
Include troubleshooting guides

API Documentation

API documentation should include details on Azure API endpoints used, authentication requirements, rate limiting and quota information, and service availability considerations.

Document Azure API endpoints used
Include authentication requirements
Provide rate limiting and quota information
Include service availability considerations

Success Criteria

Functional Requirements

The implementation should successfully invoke AppLens detectors, access Resource Health events, retrieve Azure Advisor recommendations, provide actionable insights, and handle errors gracefully.

✅ Successfully invoke AppLens detectors and retrieve results
✅ Access current and historical Resource Health events
✅ Retrieve Azure Advisor recommendations with severity levels
✅ Provide actionable insights and recommendations
✅ Handle errors and edge cases gracefully

Performance Requirements

The tools should respond to diagnostic queries within a reasonable time, handle concurrent requests efficiently, cache frequently accessed data, and scale with cluster count and data volume.

✅ Respond to diagnostic queries within reasonable time (< 30s)
✅ Handle multiple concurrent requests efficiently
✅ Cache frequently accessed data appropriately
✅ Scale with cluster count and data volume

Security Requirements

The implementation should enforce proper Azure authentication and authorization, respect Azure RBAC and subscription boundaries, protect sensitive diagnostic information, and log security events.

✅ Implement proper Azure authentication and authorization
✅ Respect Azure RBAC and subscription boundaries
✅ Protect sensitive diagnostic information
✅ Log security events and access attempts

Integration Requirements

The tools should seamlessly integrate with the existing AKS-MCP architecture, follow established code patterns, support all configured access levels, and maintain backward compatibility.

✅ Seamlessly integrate with existing AKS-MCP architecture
✅ Follow established code patterns and conventions
✅ Support all configured access levels
✅ Maintain backward compatibility

Implementation Priority

The implementation should be prioritized in phases, starting with basic AppLens detector invocation and progressing to advanced filtering and correlation features.

Phase 1: Basic AppLens detector invocation
Phase 2: Resource Health event access
Phase 3: Azure Advisor recommendation retrieval
Phase 4: Advanced filtering and correlation features
Phase 5: Performance optimization and caching

Conclusion

By integrating AppLens, Resource Health, and Azure Advisor into AKS-MCP, administrators and developers gain powerful tools for monitoring, diagnosing, and optimizing their Kubernetes deployments. These tools provide actionable insights that enable proactive issue resolution, resource utilization optimization, and maintenance of a secure and reliable environment. Following the implementation guidelines, testing requirements, and success criteria outlined in this article ensures a robust and effective integration. The phased implementation approach allows for incremental delivery of functionality, ensuring that critical features are available early in the process. This comprehensive approach to AKS monitoring and diagnostics empowers organizations to maximize the value of their Kubernetes investments.

Implementation Request: Integrating Diagnostic and Advisory Tools into AKS-MCP

1. AppLens Detector Integration

Tool: invoke_applens_detector

Tool: list_applens_detectors

2. Resource Health Event Tools

Tool: get_resource_health_status

Tool: get_resource_health_events

3. Azure Advisor Tools

Tool: get_azure_advisor_recommendations

Tool: get_advisor_recommendation_details

Technical Implementation Guidelines

Authentication and Authorization

Error Handling

Data Processing

Integration with MCP Framework

Code Structure Requirements

File Organization

Tool Registration

Configuration Support

Testing Requirements

Unit Tests

Integration Tests

Example Test Cases

Documentation Requirements

Tool Documentation

API Documentation

Success Criteria

Functional Requirements

Performance Requirements

Security Requirements

Integration Requirements

Implementation Priority

Conclusion

Tool: `invoke_applens_detector`

Tool: `list_applens_detectors`

Tool: `get_resource_health_status`

Tool: `get_resource_health_events`

Tool: `get_azure_advisor_recommendations`

Tool: `get_advisor_recommendation_details`