finch_build_container_image
AI agents invoke finch_build_container_image to trigger actions in Amazon SageMaker AI MCP Server. What it does depends on the arguments the agent supplies, and its effects often reach beyond the immediate call — builds kicked off, notifications sent, workflows started.
Building container images executes code in an isolated environment but with significant side effects: resource consumption, creation of artifacts, and potential injection of malicious code into images if arguments are compromised. This falls under Execute rather than Write because the build process itself is a triggered operation.
From the tool's definition Tool name 'finch_build_container_image' indicates building/compiling container images, which triggers external operations (container build process) whose effects depend on the input arguments (Dockerfile content, base image, build configuration).
Documented attack patterns abuse exactly the kind of access finch_build_container_image gives an agent:
PolicyLayer is an MCP gateway — it sits between your AI agents and Amazon SageMaker AI MCP Server, and nothing reaches the server without passing your rules. This is the rule we recommend for finch_build_container_image:
{
"version": "1",
"default": "deny",
"tools": {
"finch_build_container_image": {
"limits": [
{
"counter": "finch_build_container_image_rate",
"window": "minute",
"max": 10,
"scope": "grant"
}
]
}
}
}finch_build_container_image stays usable, but rate-capped — a runaway agent can't fire it dozens of times a minute. Everything else on the server is denied unless you say otherwise.
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finch_build_container_image. It is categorised as a Execute tool in the Amazon SageMaker AI MCP Server MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the Amazon SageMaker AI MCP Server MCP server in PolicyLayer and add a rule for finch_build_container_image: allow, deny, rate-limit, or require approval. Point your MCP client at the PolicyLayer proxy URL and the rule is enforced on every call, before it reaches Amazon SageMaker AI MCP Server. Nothing to install.
finch_build_container_image 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 finch_build_container_image rule in your PolicyLayer 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 PolicyLayer policy for finch_build_container_image. 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.
finch_build_container_image is provided by the Amazon SageMaker AI MCP Server MCP server (awslabs.sagemaker-ai-mcp-server). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Start from Amazon SageMaker AI MCP Server, add the rest of your stack, and see everything your agents can call. Then put policy on all of it.
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