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Mailgw

international__mailgw
Read-onlyIdempotent

Retrieve temporary email domains from Mail.gw for international data operations, providing freshness scores and source verification for audit trails.

Instructions

[International Data Agent] Get available temporary email domains from Mail.gw. Source: Mail.gw (Free API), updates monthly. Returns the Katzilla envelope { data, quality, citation } — quality scores freshness/uptime/confidence; citation carries the source URL, license, and a SHA-256 data hash for audit.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
dataYesStructured payload from the upstream source.
textNoPre-rendered text representation, when applicable.
qualityYesQuality scorecard: freshness, uptime, completeness, confidence, certainty.
citationYesProvenance block — source, license, retrieval timestamp, SHA-256 data hash, pre-formatted citation text.
Behavior4/5

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

Annotations cover read-only, non-destructive, idempotent, and open-world hints, so the description adds value by disclosing behavioral traits beyond annotations: it specifies the source ('Mail.gw'), update frequency ('monthly'), and return format details ('Katzilla envelope' with quality scores and citation info). No contradiction with annotations is present.

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 efficiently structured in two sentences: the first states the purpose and source, the second details the return format. Every sentence provides essential information without redundancy, making it front-loaded and concise.

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?

Given the tool has 0 parameters, rich annotations, and an output schema, the description is complete. It covers purpose, source, update frequency, and return format, compensating for any gaps and providing sufficient context for an agent to use the tool effectively.

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 input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description appropriately focuses on output semantics, explaining the return structure ('Katzilla envelope') and its components, which adds meaningful context beyond the schema.

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 clearly states the tool's purpose: 'Get available temporary email domains from Mail.gw.' It specifies the verb ('Get'), resource ('temporary email domains'), and source ('Mail.gw'). However, it does not explicitly differentiate from sibling tools, as none appear to be related to email domains or Mail.gw, though this is implied by the unique context.

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 by mentioning 'Free API' and 'updates monthly,' suggesting it's for retrieving static, periodically updated data. It does not provide explicit when-to-use guidance, alternatives, or exclusions, leaving the agent to infer based on the purpose.

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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