ElevenLabs

elevenlabs_create_podcast

Create Podcast. Create and auto-convert a podcast project. Currently, the LLM cost is covered by us but you will still be charged for the audio generation. In the future, you will be charged for both the LLM and audio generation costs.

Bulk support: accepts model_ids for batched execution.

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": {
      "model_id": "<model_id>",
      "mode": "<mode>",
      "source": "<source>"
    },
    "name": "elevenlabs_create_podcast"
  }
}

Input Schema

Name	Required	Description	Default
`mode`	Yes
`intro`	No
`outro`	No
`source`	Yes
`account`	No
`language`	No
`model_id`	Yes
`model_ids`	No
`highlights`	No
`callback_url`	No
`duration_scale`	No
`quality_preset`	No
`safety-identifier`	No
`instructions_prompt`	No
`apply_text_normalization`	No

Schema Changelog

Changes observed during successful MCP inspections.

First observedAug 21, 2026

TDQS

A3.8/5.0

Behavior4/5

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

Annotations only indicate not read-only, not idempotent, and not destructive, so the description adds valuable behavioral context: creation, auto-conversion, current and future pricing, and batch execution. It does not mention the response shape or whether conversion is synchronous/asynchronous, but the disclosed cost and bulk behavior go beyond the annotations.

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 description is relatively short and readable. 'Create Podcast' is somewhat redundant with the tool name, but the cost explanation and bulk-support note are useful and concise.

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

Completeness2/5

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

Given the large parameter surface, 0% schema description coverage, and no output schema, the description is not fully sufficient for correct invocation. It omits important guidance on mode variants, source structures, quality/duration enums, and the conversion lifecycle, though it does add cost and bulk context.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate for the 15 parameters. It only explains model_ids for batched execution, leaving required parameters like model_id, mode, and source—and all other optional parameters—without semantic explanation.

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 and auto-converts a podcast project, distinguishing it from sibling creation tools like elevenlabs_create_voice or elevenlabs_create_speech_engine. The verb and resource are specific and unambiguous.

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 gives clear context for when to use the tool: to create and auto-convert a podcast project. It also provides useful operational guidance by noting bulk execution via model_ids and cost considerations. It does not explicitly state when not to use it, but the purpose is well scoped.

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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