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AWS MCP Server

AWS MCP Server

by marcelobrake
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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aws_sagemaker_list_endpoints

List and filter Amazon SageMaker inference endpoints to monitor deployment status and manage machine learning models in AWS.

Instructions

List SageMaker inference endpoints.

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_endpoints"
  }
}

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 endpoints 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, and description carries full burden but adds no behavioral context. Doesn't state this is a read-only operation, doesn't explain AWS pagination behavior (NextToken pattern), credential caching, or rate limit implications despite being an AWS API call.

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. No waste, but arguably underspecified for an AWS tool where authentication and pagination behavior typically need explanation. Front-loaded with clear subject/verb/object structure.

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 standard list operation with good schema coverage, but missing expected AWS-specific context: no mention of what the response contains (endpoint ARNs, names, statuses), pagination continuation tokens (notably absent from schema), or pagination limits.

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 has 100% description coverage for all 4 parameters. The description 'List SageMaker inference endpoints' provides minimal additional context about parameter usage, meeting the baseline for high-coverage schemas.

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?

Clear verb 'List' and resource 'SageMaker inference endpoints'. However, it doesn't explicitly distinguish from sibling 'aws_sagemaker_describe_endpoint' (which gets single endpoint details vs listing multiple), though the naming convention hints at this distinction.

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?

No guidance on when to use this tool versus alternatives like 'describe_endpoint', or when filtering by status is appropriate versus retrieving all endpoints. No mention of pagination handling despite 'max_results' parameter implying pagination exists.

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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