Analyze AWS HealthOmics workflow run performance and provide optimization recommendations. This tool analyzes HealthOmics workflow runs to help users optimize: - Resource utilization patterns (CPU, memory) - Cost optimization opportunities - Performance bottlenecks - Resource allocation efficien...
Part of the AWS HealthOmics MCP Server MCP server. Enforce policies on this tool with Intercept, the open-source MCP proxy.
AI agents call AnalyzeAHORunPerformance to retrieve information from AWS HealthOmics 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 AnalyzeAHORunPerformance 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:
AnalyzeAHORunPerformance:
rules:
- action: allowSee the full AWS HealthOmics MCP Server policy for all 73 tools.
Agents calling read-class tools like AnalyzeAHORunPerformance 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.
Analyze AWS HealthOmics workflow run performance and provide optimization recommendations. This tool analyzes HealthOmics workflow runs to help users optimize: - Resource utilization patterns (CPU, memory) - Cost optimization opportunities - Performance bottlenecks - Resource allocation efficiency - Runtime optimization suggestions Use this tool when users ask about: - "How can I optimize my HealthOmics runs?" - "Why is my workflow using too many resources?" - "How can I reduce costs for my genomic workflows?" - "What resources are being wasted in my runs?" - "How can I improve workflow performance?" The tool summarizes run manifest logs containing task-level metrics and provides a structured report with recommendations for optimization. Args: ctx: MCP request context for error reporting run_ids: List of run IDs to analyze for optimization headroom: Headroom percentage for instance recommendations (default 0.20 = 20%) detailed: Include detailed task metrics JSON section (default False) aws_profile: Optional AWS profile name override aws_region: Optional AWS region override Returns: Formatted analysis string with structured manifest data and optimization recommendations. It is categorised as a Read tool in the AWS HealthOmics 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 AnalyzeAHORunPerformance. You can allow, deny, rate-limit, or validate arguments. Then run Intercept as a proxy in front of the AWS HealthOmics MCP Server MCP server.
AnalyzeAHORunPerformance 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 AnalyzeAHORunPerformance 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 AnalyzeAHORunPerformance. 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.
AnalyzeAHORunPerformance is provided by the AWS HealthOmics MCP Server MCP server (awslabs.aws-healthomics-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.