Reel-E

Record narration

request_voiceover_audio

Destructive

Records the current draft script with an AI voice (Miles, Maya, Grant or Nora). It follows request_voiceover_script or draft_narration, once the person has read the draft (read_narration_script shows its exact words) and chosen a voice. Uses 1 voice generation once approved, and the recording goes into the video only when they apply it. On a Full control connection it starts at once; otherwise it returns a confirmationId and waits for the owner's yes.

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": {
      "listingId": "<listingId>"
    },
    "name": "request_voiceover_audio"
  }
}

Input Schema

Name	Required	Description	Default
`voice`	No	The AI voice: Miles, Maya, Grant or Nora.
`listingId`	Yes	Listing id from list_listings.

Output Schema

Name	Required	Description	Default
`status`	No

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedNov 10, 2026
- addedInput schema / properties / voice / description
```
Added value: +"The AI voice: Miles, Maya, Grant or Nora."
```
First observedNov 9, 2026

TDQS

A4.6/5.0

Behavior5/5

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

Annotations cover the safety profile (non-read-only, destructive, non-idempotent), and the description adds context they do not carry: the call consumes one voice generation, the result is not committed to the video until applied, and on a Full control connection it executes immediately while otherwise it returns a confirmationId pending owner approval. That cost, gating and async-confirmation behavior is exactly the extra an agent needs for a destructive, non-idempotent operation.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The action is front-loaded in the first sentence and the remaining sentences cover prerequisites and execution semantics with no filler. The third sentence is somewhat dense with clauses, but nothing is redundant.

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?

Although an output schema exists, the description still tells the agent that a confirmationId may come back and why, and it covers cost, approval gating and the apply step. Nothing an agent needs to call this destructive, credit-consuming tool correctly is missing.

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 coverage is 100% and both parameters are documented, so the schema does the heavy lifting. The description repeats the voice names but adds no meaning about listingId or about how the chosen voice interacts with the draft, so this sits at the baseline for full-coverage schemas.

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 opens with a specific verb and resource ("Records the current draft script with an AI voice") and lists the exact voice options, so the agent can tell this apart from request_voiceover_script, draft_narration and apply_narration without opening any schema.

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

Usage Guidelines5/5

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

It explicitly places the tool in a sequence: it follows request_voiceover_script or draft_narration, requires the person to have read the draft via read_narration_script and picked a voice, and its output only reaches the video when they apply it. Both preconditions and the downstream handoff are stated rather than implied.

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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