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create_email_test

Create a disposable inbound email test session to verify SPF, DKIM, DMARC, and spam triggers for outbound messages before delivery. Get a unique single-use address valid for 60 minutes.

Instructions

Create a new IntoDNS.ai inbound email-test session. Returns a unique single-use test email address (valid 60 minutes) and a testId used by get_email_test or poll_email_test. This is an additive, non-idempotent POST: every call creates a fresh session but never modifies prior sessions. language controls result text (en/nl/de/fr, default en). Use to debug an outbound message's SPF/DKIM/DMARC, headers, and spam triggers; after sending, call poll_email_test. No auth.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
languageNoResult languageen
Behavior5/5

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

The description adds substantial behavioral context beyond the annotations: it declares this is a non-idempotent POST, is additive and never modifies prior sessions, has a 60-minute validity, and explicitly states no auth is required. These details enrich the readOnlyHint false and idempotentHint false annotations, going beyond what structured fields convey.

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 efficient and well-structured: a single introductory sentence states the core action and returns, followed by behavioral notes, parameter clarification, usage guidance, and auth status. Every sentence adds value, and the most important information is front-loaded.

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?

The description is complete for a tool with one optional parameter and no output schema. It explains the return value format, provides lifecycle context (valid 60 minutes, used by get_email_test/poll_email_test), and gives a clear end-to-end workflow. No critical information appears missing for an agent to use it correctly.

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 description coverage is 100% (the only param `language` is fully described with enum and default). The description adds minimal extra meaning ('controls result text') which is essentially a rephrase of the schema's 'Result language'. It provides no new syntax or format guidance, so it meets the baseline of 3 but does not exceed it.

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 tool creates a new inbound email-test session, names the returned artifacts (test email address, testId), and explicitly differentiates from sibling tools by referencing get_email_test and poll_email_test as follow-ups. This is a specific verb+resource description with clear scope.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It explicitly states when to use the tool ('Use to debug an outbound message's SPF/DKIM/DMARC, headers, and spam triggers') and provides the next step ('after sending, call poll_email_test'). However, it does not mention when not to use it or list alternative tools (e.g., scan_domain) for other scenarios, so it falls short of the full 'when-not/alternatives' criterion.

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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