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dgallitelli

SageMaker AI MCP Server

by dgallitelli

start_pipeline_execution_sagemaker

Starts a SageMaker pipeline execution by name with parameters for automated ML workflow runs.

Instructions

Start a SageMaker Pipeline Execution

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
parametersYesA dictionary of parameters to pass to the pipeline execution
pipeline_nameYesThe name of the SageMaker Pipeline to start execution for

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It only states 'Start a SageMaker Pipeline Execution,' implying a mutating action but providing no details about asynchronicity, return values, potential errors, or whether it requires an existing pipeline. This is a significant gap for a tool that likely triggers a long-running workflow.

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 description is a single sentence, concise and front-loaded, with no filler words. However, it is arguably under-specified for the tool's complexity, which limits its usefulness. Still, it is efficiently written and easy to parse.

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

Completeness2/5

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

Although an output schema exists (so return format need not be described), the description lacks essential context for a state-changing operation. It does not mention whether the call is asynchronous, whether it requires a pipeline to be in a certain state, or what side effects occur. This is incomplete for an agent that needs to correctly assess the impact of 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 description coverage is 100%: both 'pipeline_name' and 'parameters' have descriptions explaining their values. The tool description adds nothing beyond what the schema already provides, so it does not compensate or extend parameter understanding. A baseline score of 3 is appropriate given the schema's completeness.

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?

The description 'Start a SageMaker Pipeline Execution' clearly states the action (start) and the resource (SageMaker Pipeline Execution). It distinguishes from sibling tools like stop_pipeline_execution and describe_pipeline_execution by indicating initiation rather than modification or inspection, though it does not explicitly call out this differentiation.

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?

There is no guidance on when to use this tool versus alternatives. It does not mention prerequisites (e.g., pipeline must exist), when a start is appropriate, or that stopping/describing executions are separate operations. The description is purely a statement of the action with no contextual direction.

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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