Comprehensive canary failure analysis with deep dive into issues. Use this tool to: - Deep dive into canary failures with root cause identification - Analyze historical patterns and specific incident details - Get comprehensive artifact analysis including logs, screenshots, and HAR files - Recei...
Admin/system-level operation
Part of the CloudWatch Application Signals MCP Server MCP server. Enforce policies on this tool with Intercept, the open-source MCP proxy.
AI agents call analyze_canary_failures to retrieve information from CloudWatch Application Signals MCP Server without modifying any data. This is common in research, monitoring, and reporting workflows where the agent needs context before taking action. Because read operations don't change state, they are generally safe to allow without restrictions -- but you may still want rate limits to control API costs.
Even though analyze_canary_failures only reads data, uncontrolled read access can leak sensitive information or rack up API costs. An agent caught in a retry loop could make thousands of calls per minute. A rate limit gives you a safety net without blocking legitimate use.
Read-only tools are safe to allow by default. No rate limit needed unless you want to control costs.
tools:
analyze_canary_failures:
rules:
- action: allowSee the full CloudWatch Application Signals MCP Server policy for all 22 tools.
Agents calling read-class tools like analyze_canary_failures have been implicated in these attack patterns. Read the full case and prevention policy for each:
Other tools in the Read risk category across the catalogue. The same policy patterns (rate-limit, allow) apply to each.
Comprehensive canary failure analysis with deep dive into issues. Use this tool to: - Deep dive into canary failures with root cause identification - Analyze historical patterns and specific incident details - Get comprehensive artifact analysis including logs, screenshots, and HAR files - Receive actionable recommendations based on AWS debugging methodology - Correlate canary failures with Application Signals telemetry data - Identify performance degradation and availability issues across service dependencies Key Features: - **Failure Pattern Analysis**: Identifies recurring failure modes and temporal patterns - **Artifact Deep Dive**: Analyzes canary logs, screenshots, and network traces for root causes - **Service Correlation**: Links canary failures to upstream/downstream service issues using Application Signals - **Performance Insights**: Detects latency spikes, fault rates, and connection issues - **Actionable Remediation**: Provides specific steps based on AWS operational best practices Common Use Cases: 1. **Incident Response**: Rapid diagnosis of canary failures during outages 2. **Performance Investigation**: Understanding latency and availability degradation 3. **Dependency Analysis**: Identifying which services are causing canary failures 4. **Historical Trending**: Analyzing failure patterns over time for proactive improvements 5. **Root Cause Analysis**: Deep dive into specific failure scenarios with full context Output Includes: - Severity-ranked findings with immediate action items - Service-level telemetry insights with trace analysis - Exception details and stack traces from canary artifacts - Network connectivity and performance metrics - Correlation with Application Signals audit findings - Historical failure patterns and recovery recommendations Args: canary_name (str): Name of the CloudWatch Synthetics canary to analyze region (str, optional): AWS region where the canary is deployed. description (str, optional): User's description of the issue they are experiencing. This is matched against the knowledge base to surface relevant recommendations even when the canary error logs alone may not contain enough context. Examples: "missing runs in console", "visual monitoring baseline keeps resetting", "CloudFormation rollback failed after runtime upgrade". Returns: dict: Comprehensive failure analysis containing: - Failure severity assessment and immediate recommendations - Detailed artifact analysis (logs, screenshots, HAR files) - Service dependency health and performance metrics - Root cause identification with specific remediation steps - Historical pattern analysis and trend insights. It is categorised as a Read tool in the CloudWatch Application Signals MCP Server MCP Server, which means it retrieves data without modifying state.
Add a rule in your Intercept YAML policy under the tools section for analyze_canary_failures. You can allow, deny, rate-limit, or validate arguments. Then run Intercept as a proxy in front of the CloudWatch Application Signals MCP Server MCP server.
analyze_canary_failures is a Read tool with low risk. Read-only tools are generally safe to allow by default.
Yes. Add a rate_limit block to the analyze_canary_failures rule in your Intercept policy. For example, setting max: 10 and window: 60 limits the tool to 10 calls per minute. Rate limits are tracked per agent session and reset automatically.
Set action: deny in the Intercept policy for analyze_canary_failures. The AI agent will receive a policy violation error and cannot call the tool. You can also include a reason field to explain why the tool is blocked.
analyze_canary_failures is provided by the CloudWatch Application Signals MCP Server MCP server (awslabs.cloudwatch-applicationsignals-mcp-server). Intercept sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Deterministic policy on every MCP tool call. Per-identity grants. Full audit log.