Start a new HealthOmics run batch. Args: ctx: MCP context for error reporting workflow_id: ID of the workflow to run role_arn: IAM role ARN for the batch runs output_uri: S3 URI for output files batch_run_settings: Batch run settings with inlineSettings or s3UriSettings b...
High parameter count (18 properties)
Part of the AWS HealthOmics MCP Server MCP server. Enforce policies on this tool with Intercept, the open-source MCP proxy.
AI agents invoke StartAHORunBatch to trigger processes or run actions in AWS HealthOmics MCP Server. Execute operations can have side effects beyond the immediate call -- triggering builds, sending notifications, or starting workflows. Rate limits and argument validation are essential to prevent runaway execution.
StartAHORunBatch can trigger processes with real-world consequences. An uncontrolled agent might start dozens of builds, send mass notifications, or kick off expensive compute jobs. Intercept enforces rate limits and validates arguments to keep execution within safe bounds.
Execute tools trigger processes. Rate-limit and validate arguments to prevent unintended side effects.
tools:
StartAHORunBatch:
rules:
- action: allow
rate_limit:
max: 10
window: 60
validate:
required_args: trueSee the full AWS HealthOmics MCP Server policy for all 73 tools.
Agents calling execute-class tools like StartAHORunBatch have been implicated in these attack patterns. Read the full case and prevention policy for each:
Other tools in the Execute risk category across the catalogue. The same policy patterns (rate-limit, validate) apply to each.
StartAHORunBatch is one of the high-risk operations in AWS HealthOmics MCP Server. For the full severity-focused view — only the high-risk tools with their recommended policies — see the breakdown for this server, or browse all high-risk tools across every MCP server.
Start a new HealthOmics run batch. Args: ctx: MCP context for error reporting workflow_id: ID of the workflow to run role_arn: IAM role ARN for the batch runs output_uri: S3 URI for output files batch_run_settings: Batch run settings with inlineSettings or s3UriSettings batch_name: Optional name for the batch workflow_type: Optional workflow type (WDL, NEXTFLOW, CWL) workflow_version_name: Optional version name of the workflow parameters: Optional default parameters for all runs storage_type: Optional storage type (STATIC or DYNAMIC) storage_capacity: Optional storage capacity in GB run_group_id: Optional run group ID cache_id: Optional run cache ID cache_behavior: Optional cache behavior retention_mode: Optional retention mode request_id: Optional idempotency token tags: Optional tags for the batch aws_profile: Optional AWS profile name override aws_region: Optional AWS region override Returns: Dictionary containing id, arn, status, uuid, and tags, or error dict. It is categorised as a Execute tool in the AWS HealthOmics MCP Server MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Add a rule in your Intercept YAML policy under the tools section for StartAHORunBatch. 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.
StartAHORunBatch is a Execute tool with high risk. Execute tools should be rate-limited and have argument validation enabled.
Yes. Add a rate_limit block to the StartAHORunBatch 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 StartAHORunBatch. 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.
StartAHORunBatch 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.