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@aiwerk/mcp-server-elevenlabs

@aiwerk/mcp-server-elevenlabs

by AIWerk
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Hybrid

update_segment_language

Updates the language assigned to a single segment in an ElevenLabs dubbing project. This credit-spending operation is deprecated upstream.

Instructions

Modify A Single Segment Spends ElevenLabs credits. Deprecated upstream.

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": {
      "dubbing_id": "<dubbing_id>",
      "segment_id": "<segment_id>",
      "language": "<language>"
    },
    "name": "update_segment_language"
  }
}

Input Schema

NameRequiredDescriptionDefault
textNo
end_timeNo
languageYesID of the language.
dubbing_idYesID of the dubbing project.
segment_idYesID of the segment
start_timeNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

C2.9/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=false, idempotentHint=false, and openWorldHint=true, so the mutation profile is covered structurally. The description adds two facts not in the annotations: that the call spends ElevenLabs credits (cost implication) and that the tool is deprecated upstream (lifecycle status). Those are genuine behavioral additions beyond structured fields.

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?

Two terse clauses, front-loaded with the action and then the deprecation caveat; nothing is padded. The phrasing is slightly garbled ("Modify A Single Segment Spends ElevenLabs credits"), which costs a point but not efficiency.

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?

For a non-idempotent, credit-consuming mutation with 6 parameters and no output schema, the description omits parameter meaning, required-field obligations, and any indication of what the response contains. The credit and deprecation notes are helpful but leave the definition substantially incomplete.

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 only 50% across 6 parameters, and the description explains none of them. It gives no hint about what text, language, dubbing_id, segment_id, start_time, or end_time do, so it fails to compensate for the undocumented half of the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose3/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description says it modifies a single segment, which is a verb+resource, but it never states the actual resource being changed (the segment's language) that the tool name implies. An agent must open the schema to learn that language, text, and timing fields are involved. It is not a tautology, but the purpose is only vaguely conveyed.

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

Usage Guidelines2/5

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

"Deprecated upstream" is a useful caution flag but not usage guidance. Nothing says when to use this versus siblings like dubbing_target_transcript_segment_update, dubbing_transcript_segment_update, or migrate_segments, all of which operate on similar dubbing transcript segment data.

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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