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AWS MCP Server

AWS MCP Server

by marcelobrake
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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aws_sagemaker_list_training_jobs

Retrieve and filter Amazon SageMaker training jobs by status and region to monitor machine learning model development progress in AWS environments.

Instructions

List SageMaker training jobs.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "aws_sagemaker_list_training_jobs"
  }
}

Input Schema

NameRequiredDescriptionDefault
profileNoAWS profile name from ~/.aws/config (e.g., 'default', 'production')
regionNoAWS region override (e.g., 'us-east-1', 'sa-east-1')
status_equalsNoFilter by status
max_resultsNoMaximum jobs to return

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

C2.9/5.0
Behavior2/5

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

No annotations provided, so description bears full burden. Fails to disclose pagination behavior, whether results are time-limited, required IAM permissions beyond the profile parameter, or if the operation is read-only (implied by 'List' but not confirmed).

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?

Extremely concise at 4 words with no wasted content. However, the brevity borders on under-specification rather than efficient information density.

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

Completeness3/5

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

Adequate for a simple list operation with fully documented schema parameters, but lacks output format details or pagination guidance given no output schema exists.

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% with clear descriptions for all 4 parameters (profile, region, status_equals, max_results). Description adds no parameter-specific context beyond the schema, warranting baseline score.

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

Purpose4/5

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

States specific verb (List) and resource (SageMaker training jobs) clearly. However, does not distinguish from sibling SageMaker list operations (e.g., list_endpoints, list_notebook_instances) or clarify scope.

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

Usage Guidelines2/5

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

Provides no guidance on when to use this tool versus sibling SageMaker tools or other AWS listing operations. No prerequisites or filtering strategy mentioned despite the presence of optional filter parameters.

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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