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aws-samples

AWS Health MCP Server

Official
by aws-samples

get_service_health

Check current AWS service health by retrieving active and upcoming events with descriptions and timelines, so you can identify disruptions or planned maintenance.

Instructions

Get current AWS service health events.

Returns active and upcoming AWS health events with descriptions and timelines.

Example prompts:

  • "Show me all current AWS service health issues"

  • "What's the current status of AWS services?"

  • "Are there any active AWS service disruptions?"

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv2.0.0

TDQS

A3.5/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It discloses the result scope ('active and upcoming' events with descriptions and timelines'), which is meaningful behavioral context, and 'Get' implies a read-only operation. It does not state auth/permission needs, whether results are cached or real-time, or any rate limits, so coverage is partial rather than complete.

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 core purpose is front-loaded in the first sentence, followed by a one-line return summary and three example prompts. The examples are somewhat redundant with each other but serve semantic-matching purposes, so the overall structure is efficient with minimal waste.

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?

An output schema exists and there are no parameters, so the description needn't document returns or arguments, and it covers those adequately. What is missing is disambiguation among the ten sibling tools, which is the main context an agent needs and which the description does not supply.

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

Parameters4/5

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

The tool takes zero parameters, so the baseline of 4 applies. The description correctly implies no filtering arguments are available (it returns all active/upcoming events unconditionally), which is a useful clarification for an agent that might otherwise expect a service-name filter.

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 a specific verb and resource ('Get current AWS service health events') and clarifies scope as 'active and upcoming' events, which implicitly distinguishes it from completed-event tools. However, with ten siblings including near-namesakes like get_service_events, get_org_service_health, and get_completed_events, the description never names or contrasts against any alternative, leaving the agent to guess which endpoint to call.

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?

The example prompts ('Show me all current AWS service health issues', 'What's the current status of AWS services?') give concrete usage contexts, so the intended invocation scenario is reasonably implied. But there is no explicit when-to-use guidance, no exclusions, and no mention of when a sibling tool would be the better choice.

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