Analyzes a CloudWatch log group for anomalies, message patterns, and error patterns within a specified time window. This tool performs an analysis of the specified log group by: 1. Discovering and checking log anomaly detectors associated with the log group 2. Retrieving anomalies from those det...
Part of the AWS Labs CloudWatch MCP Server MCP server. Enforce policies on this tool with Intercept, the open-source MCP proxy.
AI agents call analyze_log_group to retrieve information from AWS Labs CloudWatch 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_log_group 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_log_group:
rules:
- action: allowSee the full AWS Labs CloudWatch MCP Server policy for all 19 tools.
Agents calling read-class tools like analyze_log_group 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.
Analyzes a CloudWatch log group for anomalies, message patterns, and error patterns within a specified time window. This tool performs an analysis of the specified log group by: 1. Discovering and checking log anomaly detectors associated with the log group 2. Retrieving anomalies from those detectors that fall within the specified time range 3. Identifying the top 5 most common message patterns 4. Finding the top 5 patterns containing error-related terms Usage: Use this tool to detect anomalies and understand common patterns in your log data, particularly focusing on error patterns that might indicate issues. This can help identify potential problems and understand the typical behavior of your application. Returns: -------- A LogsAnalysisResult object containing: - log_anomaly_results: Information about anomaly detectors and their findings * anomaly_detectors: List of anomaly detectors for the log group * anomalies: List of anomalies that fall within the specified time range - top_patterns: Results of the query for most common message patterns - top_patterns_containing_errors: Results of the query for patterns containing error-related terms (error, exception, fail, timeout, fatal). It is categorised as a Read tool in the AWS Labs CloudWatch 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_log_group. You can allow, deny, rate-limit, or validate arguments. Then run Intercept as a proxy in front of the AWS Labs CloudWatch MCP Server MCP server.
analyze_log_group 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_log_group 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_log_group. 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_log_group is provided by the AWS Labs CloudWatch MCP Server MCP server (awslabs.cloudwatch-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.