Describe a SageMaker HyperPod cluster. Args: ctx: MCP context cluster_name: REQUIRED - Target cluster for describe cluster api region_name: REQUIRED - AWS region name profile_name: AWS profile name (optional) ## Fallback Options: - If this tool fails, advise using AWS SageMaker ...
Part of the Amazon SageMaker AI MCP Server MCP server. Enforce policies on this tool with Intercept, the open-source MCP proxy.
AI agents call describe_hp_cluster to retrieve information from Amazon SageMaker AI 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 describe_hp_cluster 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:
describe_hp_cluster:
rules:
- action: allowSee the full Amazon SageMaker AI MCP Server policy for all 4 tools.
Agents calling read-class tools like describe_hp_cluster 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.
Describe a SageMaker HyperPod cluster. Args: ctx: MCP context cluster_name: REQUIRED - Target cluster for describe cluster api region_name: REQUIRED - AWS region name profile_name: AWS profile name (optional) ## Fallback Options: - If this tool fails, advise using AWS SageMaker CLI option: `aws sagemaker describe-cluster --cluster-name <name> --region <cluster_region>` - Or as another alternative, advise checking directly in the SageMaker HyperPod console (Amazon SageMaker AI → HyperPod Clusters → Cluster Management → select cluster) Returns: describe cluster response. It is categorised as a Read tool in the Amazon SageMaker AI 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 describe_hp_cluster. You can allow, deny, rate-limit, or validate arguments. Then run Intercept as a proxy in front of the Amazon SageMaker AI MCP Server MCP server.
describe_hp_cluster 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 describe_hp_cluster 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 describe_hp_cluster. 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.
describe_hp_cluster is provided by the Amazon SageMaker AI MCP Server MCP server (awslabs.sagemaker-ai-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.