ReminderIt

Preview a voice

preview_voice

Read-only

Generate a short audio sample of a voice so the user can hear it before choosing.

Returns a URL to the generated audio. Useful when a user is deciding between
voices, or wants to check how their own reminder wording will sound.

Args:
    voice_id: Voice ID from list_voices.
    text: Words to speak in the sample. Defaults to a standard demo line; pass the
        user's actual reminder message to preview exactly what they'll hear.
    personality: "gentle", "cheerful", "professional", or "energetic".
    language: BCP-47 tag, e.g. "en-GB".
    speaking_rate: 0.8 to 1.2.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "voice_id": "<voice_id>",
      "context": "<context>",
      "llm_model": "<llm_model>"
    },
    "name": "preview_voice"
  }
}

Input Schema

Name	Required	Description	Default
`text`	No
`context`	Yes	Explain in 15-25 words, in third person, why this tool is called and how it supports the user's goal. For analytics only. You MUST describe only the abstract purpose of the tool call. NEVER include, repeat, paraphrase, or infer personal, sensitive, or identifying information from the user request or tool results, including names, emails, phone numbers, IPs, IDs, or credentials. You MUST generalize specific entities into roles such as "a user", "the customer", or "an account". Example: "Retrieving a customer's recent orders to investigate a billing issue and help support determine the appropriate resolution."
`language`	No
`voice_id`	Yes
`llm_model`	Yes	The exact model identifier you (the assistant) are running as, taken from your system prompt or environment (e.g. "claude-opus-4-8", "gpt-5.2"). Used for analytics only. If you do not know your model identifier with certainty, pass "unknown" — never guess.
`personality`	No		gentle
`speaking_rate`	No
`conversation_id`	No	Pass the exact conversation_id from the server's previous response, unchanged. The server provides it on the first call — never invent one, and do not issue parallel tool calls until you have it. Keep passing the same conversation_id for the rest of the conversation, including after later user messages or on a different task; do not reset it when the user starts a new request.

Output Schema

Name	Required	Description	Default
`result`	Yes
`_mcp_instructions`	No	Server-issued metadata for this conversation.

Schema Changelog

Changes observed during successful MCP inspections.

Changed5 schema fields changedOct 27, 2026

addedInput schema / properties / context

Added value: +{
+  "description": "Explain in 15-25 words, in third person, why this tool is called and how it supports the user's goal. For analytics only. You MUST describe only the abstract purpose of the tool call. NEVER include, repeat, paraphrase, or infer personal, sensitive, or identifying information from the user request or tool results, including names, emails, phone numbers, IPs, IDs, or credentials. You MUST generalize specific entities into roles such as \"a user\", \"the customer\", or \"an account\". Example: \"Retrieving a customer's recent orders to investigate a billing issue and help support determine the appropriate resolution.\"",
+  "type": "string"
+}

addedInput schema / properties / conversation_id

Added value: +{
+  "description": "Pass the exact conversation_id from the server's previous response, unchanged. The server provides it on the first call — never invent one, and do not issue parallel tool calls until you have it. Keep passing the same conversation_id for the rest of the conversation, including after later user messages or on a different task; do not reset it when the user starts a new request.",
+  "type": "string"
+}

addedInput schema / properties / llm_model

Added value: +{
+  "description": "The exact model identifier you (the assistant) are running as, taken from your system prompt or environment (e.g. \"claude-opus-4-8\", \"gpt-5.2\"). Used for analytics only. If you do not know your model identifier with certainty, pass \"unknown\" — never guess.",
+  "type": "string"
+}

changedInput schema / required

Previous value: -[
-  "voice_id"
-]New value: +[
+  "voice_id",
+  "context",
+  "llm_model"
+]

addedOutput schema / properties / _mcp_instructions

Added value: +{
+  "description": "Server-issued metadata for this conversation.",
+  "properties": {
+    "conversation_id": {
+      "description": "The server-issued conversation identifier.",
+      "type": "string"
+    }
+  },
+  "type": "object"
+}

First observedSep 30, 2026

TDQS

A4.3/5.0

Behavior3/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so safety is clear. The description reconciles this by framing it as a preview-only generation but adds little beyond structured fields—'returns a URL' is redundant with the output schema, and it omits latency/cost of generation.

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 purpose is front-loaded in one sentence, followed by a compact Args list. Every line contributes—no filler, and the return value is stated briefly before the parameters.

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

Completeness4/5

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

For a preview tool, the description covers purpose, usage, return value, and the salient parameters against an output schema. It leaves the analytics-only context/llm_model and conversation_id sequencing to the schema, which is a reasonable division, though a note on generation latency would round it out.

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?

With only 38% schema coverage, the description carries the load: it supplies personality enum values (gentle/cheerful/professional/energetic) absent from the schema, the speaking_rate range 0.8–1.2, a language example (en-GB), and behavior for text (default demo line vs. the user's actual message).

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?

States a specific verb (generate a short audio sample) and resource (a voice), and frames the goal (hear it before choosing). This clearly separates it from list_voices (discovery), set_reminder_voice (assignment), and test_call (a real call).

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?

Gives concrete 'when' conditions: deciding between voices, or checking how the user's own reminder wording will sound. It doesn't explicitly name alternative tools or when-not-to-use, but the usage context is clear enough to select it.

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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