Score a YouTube channel's upload pattern against YouTube's inauthentic-content policy.
Scored across 1 tool
With a single tool there is no possibility of tool-to-tool confusion; the one tool's purpose (channel slop-risk scoring) is stated explicitly and unambiguously.
The lone name is descriptive snake_case (check_channel_slop_risk), following a clear verb_noun_object pattern with no competing conventions to conflict with.
One tool is thin even for a narrow scoring service; the scope is well-defined but there is no room for supporting operations, making the surface borderline under-scoped.
The tool fully covers its single stated operation (score one channel from RSS), but there is no batch/channel-localized lookup, comparison across channels, or follow-up retrieval surface, leaving notable gaps for multi-channel or longitudinal analysis use cases.
Score a YouTube channel's recent upload pattern (title/description repetition, upload-cadence regularity, AI-disclosure language) against YouTube's inauthentic-content / mass-production demonetization policy. Reads the channel's own real public RSS feed (last 15 uploads) -- grounded in actual upload history, not a guess. Accepts a handle (e.g. '@somechannel'), a channel URL, or a bare UC... channel ID.
| Name | Required | Description | Default |
|---|---|---|---|
| channel | Yes | YouTube channel handle, URL, or channel ID, e.g. '@somechannel' |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full behavioral burden. It discloses key traits: data comes from the channel's real public RSS feed (last 15 uploads), is grounded in actual upload history rather than estimation, and the scoring dimensions and policy basis. It doesn't specify return format or rate/error behavior, but the provenance disclosure is substantive.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Front-loads the core purpose and keeps the description to three tight sentences. The final sentence duplicates the schema's parameter description, which is slightly redundant, but overall the structure is efficient and informative.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no output schema and no annotations, the description does a good job covering purpose, data source, scoring criteria, and input flexibility. The main gap is that it does not describe what the returned score or output looks like, which an agent might need. Otherwise it is complete enough for correct invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, and the single parameter is fully documented in the schema. The description repeats the accepted input formats (handle, URL, bare UC... ID) with an example, but adds no meaning beyond what the schema already provides. Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
States a specific verb ('Score') and resource ('YouTube channel's recent upload pattern'), enumerates the exact dimensions scored (title/description repetition, upload-cadence regularity, AI-disclosure language), and names the policy standard used. An agent can immediately tell what the tool does without opening the schema.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Clearly conveys the context for use: assessing a channel's demonetization risk under YouTube's inauthentic-content policy, using real public upload history. There are no sibling tools to differentiate from and no explicit when-not-to-use guidance, but the intended scenario is unambiguous.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
check_channel_slop_riskAudits any YouTube channel against its own history. Every claim carries its sample size.
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