Amazon SageMaker Well-Architected MCP Server

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validate_all_resources

Scan all SageMaker endpoints, training jobs, notebook instances, and models in an AWS region against Well-Architected pillars. Generates a comprehensive HTML report with aggregated findings.

Instructions

Validate all SageMaker resources in a region against Well-Architected pillars.

    Scans all endpoints, training jobs, notebook instances, and models in the
    specified region. Returns an on-screen summary and generates a single
    comprehensive HTML report (wa-report.html) with all findings.

    Args:
        ctx: MCP context
        region_name: AWS region name (default: us-east-1)
        profile_name: AWS profile name (optional)

    Returns:
        ValidateAllResponse with aggregated findings and path to HTML report

Input Schema

TableJSON Schema

Name	Required	Description	Default
`region_name`	No	AWS region name. Default is us-east-1.	us-east-1
`profile_name`	No	AWS profile name. If not provided, uses the default profile.

Output Schema

TableJSON Schema

Name	Required	Description
`content`	Yes	Response content
`isError`	No	Whether this is an error response
`summary`	Yes	Aggregated findings summary by pillar
`findings`	Yes	List of all findings
`total_findings`	Yes	Total number of findings
`resources_validated`	Yes	List of validated resource names

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Sep 6, 2026

TDQS

A3.9/5.0

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full disclosure burden and does reasonably well: it names the scan scope, states the on-screen summary behavior, and discloses the file-generation side effect (wa-report.html). It does not cover credential requirements, runtime duration, or failure modes, but the most decision-relevant behaviors are disclosed.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The one-sentence purpose is front-loaded, followed by a compact paragraph on scope and outputs. The Args and Returns sections are slightly redundant with the input and output schemas, but the docstring format is standard and there is no padding or filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a region-wide multi-resource scan, the description covers the essentials: what is scanned, what artifacts are produced (on-screen summary, HTML report), and what the response contains. Since an output schema exists for ValidateAllResponse, return-value detail is already structured. Missing are performance/cost expectations and sibling routing, but the description is complete enough for safe invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the schema already documents both parameters fully, including defaults and the profile fallback behavior. The Args section merely restates the schema descriptions and adds no new semantic detail. Baseline 3 applies because the structured data carries the load.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The opening sentence states a specific verb (validate), resource (all SageMaker resources in a region), and standard (Well-Architected pillars), then enumerates the scanned types: endpoints, training jobs, notebook instances, and models. This scope clearly differentiates it from siblings validate_sagemaker_resource (singular resource) and validate_all_endpoints (endpoints only).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Usage context is implied by the scope statement — an agent can infer this is the broadest validation sweep — but the description never explicitly says when to prefer it over validate_sagemaker_resource or validate_all_endpoints. There is no when-not-to-use guidance or mention of sibling alternatives. The resource enumeration gives partial routing signal, but the choice rule is left to inference.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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