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kubeopsai

k8s-telemetry-mcp

by kubeopsai

get_database_insights

Analyze RDS or ElastiCache performance over a selected time window to identify bottlenecks and optimize database health.

Instructions

Get database performance insights for RDS or ElastiCache.

Args: db_identifier: RDS instance/cluster ID or ElastiCache cluster ID db_type: 'rds' or 'elasticache' timeframe_minutes: Time window to analyze (1-1440)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
db_typeNords
db_identifierYes
timeframe_minutesNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv1.2.6

TDQS

A4.2/5.0
Behavior3/5

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

With no annotations, the description carries the transparency burden. The word 'Get' implies a read-only operation, and supported services and the time-window limit (1-1440) are disclosed. However, it does not describe what happens on invalid identifiers, whether permissions are needed, or how the returned output is structured.

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

Conciseness5/5

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

The description is compact and front-loaded with the core purpose, followed by a clean Args list. Every line adds necessary information, with no repetition or filler.

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

Completeness5/5

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

For a read-only insight tool with a single required parameter, the description covers supported services, parameter semantics, and constraints. The presence of an output schema means return values are already specified structurally, so no further output explanation is required.

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

Parameters5/5

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

The input schema contains no property descriptions, but the description compensates fully by explaining every parameter: db_identifier is the instance/cluster ID, db_type has allowed values 'rds' and 'elasticache', and timeframe_minutes is the analysis window with its valid range. This is exactly what an agent needs to invoke the tool correctly.

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 description clearly states the action ('Get'), the resource ('database performance insights'), and the scope ('RDS or ElastiCache'). This makes it readily distinguishable from the sibling tools, which focus on logs, metrics, traces, and cluster health rather than database-specific insights.

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 description implies the tool is relevant for RDS or ElastiCache performance investigations, but it does not explicitly say when to prefer it over alternatives like query_prometheus or get_pod_metrics. There is no exclusion or alternative-routing guidance.

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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