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Faceless / AI YouTube channel reality check

faceless_youtube_reality_check
Read-onlyIdempotent

Seven multiple-choice answers about a faceless or AI YouTube channel plan. Returns HIGH / MED / LOW expectation and monetization-policy risk with signals and myths to drop. Pushes back on viral '$10k/month with AI YouTube' claims. Not a ban prediction.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nicheYesNiche.
visualYesMain visual style.
scriptsYesHow scripts are made.
timelineYesExpected timeline.
estimatesYesHow VidIQ / SocialBlade earnings estimates are treated.
voiceoverYesMain voiceover.
revenueTimingYesWhen money is expected relative to the YouTube Partner Program.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.7/5.0
Behavior4/5

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

Annotations already declare readOnly, idempotent, closed-world safety, so the description is free to focus on output semantics: HIGH/MED/LOW rating, signals, and myths to drop. It adds real context via the anti-hype stance and the explicit non-goal. Missing detail on how the answers map to the rating, but the safety profile is covered.

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?

Three tight sentences, front-loaded with what the tool does and what it returns. The '$10k/month' framing reinforces intent rather than padding, though it borders on positioning rhetoric rather than invocation guidance.

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?

With no output schema, the description correctly carries the return-value burden (rating plus signals and myths) and states the scope boundary. An agent has enough to invoke correctly; only the rating derivation and per-parameter mapping remain unstated.

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?

All seven parameters are fully described in the schema with enums (100% coverage), so the schema does the heavy lifting. The description only alludes to 'seven multiple-choice answers' without adding meaning to any individual field. Baseline 3 is appropriate.

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: a seven-question reality check on faceless/AI YouTube plans that returns a HIGH/MED/LOW expectation and risk rating. This distinguishes it from generic siblings like viral_attention_quiz, though it never names a specific alternative. The scope is clear and concrete.

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?

The closing phrase 'Not a ban prediction' usefully excludes a nearby misinterpretation, and the framing implies usage for people planning faceless/AI channels. But it gives no explicit when-to-use or how to choose between it and siblings such as viral_attention_quiz or ads_policy_notice_risk_check.

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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