audit_youtube_channelScores a YouTube channel out of 100 from its public catalogue and returns the one thing to fix first. Use this when asked how a channel is doing, or to compare a channel against its own history rather than against other channels.
| Name | Required | Description | Default |
|---|---|---|---|
| channel | Yes | A YouTube channel: @handle, channel URL, channel id (UC…), or the channel name. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false. The description adds meaningful behavioral context by explaining it scores from the 'public catalogue' and returns a prioritized fix, which goes beyond the annotations. No contradiction with annotations.
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?
The description is two sentences, front-loaded with the core purpose and outcome, followed by usage guidance. Every word earns its place with no filler or repetition.
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?
Given the tool's simplicity (single parameter, read-only, idempotent) and lack of an output schema, the description adequately covers purpose, usage, and output format ('score out of 100' and 'one thing to fix first'). Minor gaps like error behavior or definition of 'public catalogue' are not critical for this complexity.
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?
The input schema has 100% coverage, with a detailed description of the 'channel' parameter (accepting @handle, URL, channel id, or name). The description adds no further parameter-specific detail, so the baseline score of 3 applies since the schema already does the heavy lifting.
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?
The description uses a specific verb 'Scores' with the resource 'YouTube channel' and clearly defines the output: 'out of 100' and 'returns the one thing to fix first'. This distinguishes it from sibling tools like check_monetization_progress or get_channel_patterns, which focus on different aspects.
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?
The description provides explicit usage context: 'Use this when asked how a channel is doing, or to compare a channel against its own history rather than against other channels.' This includes a clear exclusion ('rather than against other channels'), though it does not name alternative tools, leaving some ambiguity about when to pick a sibling.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool targets a distinct aspect of YouTube growth: audit gives an overall score, monetization tracks subs/views, patterns analyzes channel-specific performance, brief creates a next-video plan, and ab test suggests title experiments. There is no overlap or ambiguity in their purposes.
All tool names follow a consistent verb_noun pattern (audit, check, get, get, suggest), using clear action words and camelCase separation. The naming is uniform and predictable.
Five tools is well-scoped for a YouTube growth server, covering assessment, progress tracking, pattern analysis, planning, and experimentation without unnecessary bloat or missing essentials.
The tool set covers a complete growth workflow: audit status, check monetization, identify patterns, plan next video, and test improvements. A minor gap is the lack of a tool for tracking changes over time or comparing across channels, but the core domain is well covered.