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lee-s-dev

youtube-research-mcp

by lee-s-dev

analyze_channel

Collect transcripts and comments from a YouTube channel's recent videos to analyze content, engagement, and audience feedback.

Instructions

Collect transcripts and comments from a specific YouTube channel's recent videos.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
channel_id_or_handleYes
max_videosNo
max_comments_per_videoNo
languagesNo
include_segmentsNo
include_commentsNo
min_comment_lengthNo
min_like_countNo
force_refreshNo
min_duration_secondsNo
max_duration_secondsNo
published_afterNo
published_beforeNo
exclude_shortsNo
min_view_countNo
max_transcript_charsNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior2/5

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

No annotations are provided, so the description carries the full burden. It mentions data collection (transcripts, comments) but omits any behavioral details such as read-only nature, caching, rate limits, or authentication requirements. The description is too brief given the lack of annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness2/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single sentence that is under-specified for the tool's complexity. While concise, it fails to provide necessary context and is not well-structured for a tool with many parameters.

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?

Given the large parameter count and absence of schema descriptions or annotations, the description is inadequate. It does not explain defaults, required parameters, filtering logic, or any caveats, leaving the agent with insufficient information to use the tool correctly.

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

Parameters1/5

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

Schema description coverage is 0% and the description adds no meaning to the parameters. With 16 parameters, this is a critical gap—the agent has no guidance on the role of each parameter beyond their names and types.

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?

The description clearly states the verb 'Collect' and the resources 'transcripts and comments from a specific YouTube channel's recent videos', distinguishing it from single-video tools like get_transcript or get_video_comments. However, 'recent' is ambiguous given the date-range parameters, and sibling differentiation is not explicit.

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?

The description provides no guidance on when to use this tool versus alternatives like analyze_videos or collect_video_discussion. No when-to-use or when-not-to-use context is given.

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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