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YouTube Dubbing MCP

YouTube Dubbing MCP

by RTY2345RR
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

dubbing_list_projects

Read-onlyIdempotent

List saved dubbing projects on this machine, ordered newest first, to locate and manage prior YouTube dubbing work.

Instructions

List dubbing projects saved on this machine, newest first.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "dubbing_list_projects"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.7/5.0
Behavior3/5

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

Annotations already declare readOnlyHint, idempotentHint, non-destructive, and closed-world, so the safety profile is covered. The description adds genuine behavioral context with the 'newest first' sort order and the local-machine scope, but says nothing about pagination/limits, whether archived or in-progress projects are included, or result size.

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?

A single sentence with no filler, and the most important qualifier (local machine) and the ordering are both front-loaded. Nothing could be cut without losing information.

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 trivial zero-parameter list tool with an output schema and full annotation coverage, the description covers what is needed to call it correctly. The only remaining gap is result-volume behavior (pagination or caps), which an agent might want to know before relying on a complete listing.

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

Parameters4/5

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

The tool takes zero parameters, so the baseline of 4 applies and there is no parameter meaning to add or omit. The description correctly does not invent parameters such as filters or sort options that the schema does not offer.

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

Purpose4/5

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

States a specific verb and resource ('List dubbing projects') plus a scope qualifier ('saved on this machine') and an ordering rule ('newest first'). The resource alone separates it from siblings like dubbing_list_voices or dubbing_project_status, but it never explicitly contrasts with them.

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

Usage Guidelines3/5

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

Usage is only implied: an agent can infer this is the discovery entry point for obtaining projects before calling tools like dubbing_get_segments or dubbing_render. There is no explicit 'use this when' statement, no exclusions, and no named alternative for project lookup.

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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