Skip to main content
Glama

starlink_alerts

Retrieve active Starlink dish alerts with human-readable descriptions. Covers hardware, connectivity, environment, and policy flags to identify current issues.

Instructions

Get active dish alerts with human-readable descriptions.

Checks 22 alert flags covering hardware (motors, thermal), connectivity (ethernet speed, signal), environment (water, obstruction), and policy (roaming, movement). Only active alerts are shown.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.0

TDQS

A3.8/5.0
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 of behavior. It discloses the filtering behavior ('Only active alerts are shown') and the human-readable format, which is genuinely useful. However, it doesn't touch on connection requirements, availability considerations, or any other behavioral environment; with a 0-vs-machine summary that has no annotations, that is an acceptable but not exceptional showing.

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?

Two sentences, each carrying weight: the lead states the verb/resource/output format, and the second details the 22-flag taxonomy and the only-stateful-actives filter. Nothing is wasted and the front-loaded opening immediately tells the agent what it gets.

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?

With 0 parameters and an output schema present, the description fully covers the decisions an agent needs to make: whether this tool matches the user's need (alerts/conditions), which domains it touches (hardware, connectivity, environment, policy), and the shape of what is returned (human-readable active alerts). Nothing needed for correct invocation is missing.

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?

There are zero parameters, so the description has nothing to add and gets the baseline of 4. The empty input schema and 100% coverage mean the structured data is already complete.

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 names a specific verb and resource ('Get active dish alerts') and adds concrete scope by enumerating the 22 alert flags across four categories (hardware, connectivity, obstruction, policy). It is clearly differentiated from most siblings by content, but it never explicitly distinguishes itself from near-neighbors like starlink_status or starlink_diagnostics, which overlap in territory.

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 usage context (when you suspect an issue, this surfaces active faults across those sensor categories) but gives no explicit exclusions or alternatives. With seven siblings including starlink_obstruction, which overlaps on the 'obstruction' flag category, an explicit 'use starlink_obstruction instead for detailed obstruction data' would be a meaningful upgrade.

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