shiply

Create an email demand test

create_test

Provision a demand test in one call: deploys a landing page with a native email-capture form and creates a confirmed-subscriber segment. Returns testId + live siteUrl. Share the siteUrl to collect signups; each signup gets a double-opt-in confirmation. Read progress with get_test_status.

Input Schema

TableJSON Schema

Name	Required	Description
`cta`	No	call-to-action button label
`sub`	No	subheadline / supporting line
`idea`	Yes	the product/idea name
`price`	No	price to display, e.g. "$29/mo"
`headline`	Yes	landing-page headline

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

Changed5 schema fields changedJun 23, 2026

addedInput schema / properties / cta / description
```
Added value: +"call-to-action button label"
```
addedInput schema / properties / headline / description
```
Added value: +"landing-page headline"
```

addedInput schema / properties / price / description

Added value: +"price to display, e.g. \"$29/mo\""

addedInput schema / properties / sub / description
```
Added value: +"subheadline / supporting line"
```

changedOutput schema / (root)

Previous value: -nullNew value: +{
+  "$schema": "http://json-schema.org/draft-07/schema#",
+  "additionalProperties": false,
+  "properties": {
+    "result": {}
+  },
+  "required": [
+    "result"
+  ],
+  "type": "object"
+}

First observedJun 23, 2026

TDQS

A4.3/5.0

Behavior4/5

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

Beyond the idempotentHint=false annotation, the description discloses the full workflow: deployment, subscriber segment creation, double-opt-in confirmation for signups, and return of testId and siteUrl. No contradictions with annotations.

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 concise, with two sentences that front-load the primary action and follow with essential details. No wasted words.

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 presence of an output schema and the tool's complexity, the description covers all necessary context: what it does, what it returns, and how to use the results. It is self-contained.

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%, so baseline is 3. The description does not add extra semantics to the parameters beyond what the schema already provides.

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 it provisions a demand test, deploys a landing page with a native email-capture form, creates a confirmed-subscriber segment, and returns testId and siteUrl. It is distinct from sibling tools like list_tests and get_test_status.

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?

The description explains the use case (one-call provisioning of a demand test) and suggests sharing the siteUrl and monitoring progress with get_test_status. It does not explicitly mention when not to use or alternatives, but the context is clear.

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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