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mohamdben-yahia

YouTube MCP Server

compare_channels

Compare up to 10 YouTube channels side-by-side to benchmark subscriber growth, average views, upload frequency, and engagement efficiency, then identify category leaders.

Instructions

Perform a head-to-head benchmarking comparison between 2 to 10 competing YouTube channels.

Compares subscriber growth velocity, recent average views, upload cadence, views-to-subscriber efficiency, and highlights category winners.

Args: channel_handles: List of 2 to 10 channel handles (e.g. ['@channel1', '@channel2']) or Channel IDs.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
channel_handlesYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv1.1.0

TDQS

A4.3/5.0
Behavior3/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states what metrics are compared and that category winners are highlighted, but it does not explain return structure, data freshness, rate limits, or side effects (likely none). The output schema may cover some of this, but the description itself offers limited transparency beyond the core operation.

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?

The description is tight and front-loaded with the central purpose. The metrics list is informative, and the Args section is concise with only one parameter. No redundant filler sentences exist.

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 single-parameter comparison tool, the description covers the input semantics and the nature of the output qualitatively. An output schema exists, so the description need not list return fields. Minor gaps like the exact lookback window or data source for 'recent' views prevent a perfect score, but the tool is well specified for an agent to use correctly.

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

Parameters5/5

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

Schema description coverage is 0%, so the description must fully document the parameter. It does: channel_handles is described as a list of 2–10 channel handles or Channel IDs, with a concrete example format. This adds exactly the meaning the schema lacks.

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

Purpose5/5

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

The description opens with a specific verb phrase 'Perform a head-to-head benchmarking comparison between 2 to 10 competing YouTube channels,' clearly naming the resource and scope. It goes further to list the exact metrics compared (subscriber growth velocity, recent average views, upload cadence, views-to-subscriber efficiency), which distinguishes it from sibling tools like get_channel_details or audit_channel_strategy.

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

Usage Guidelines4/5

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

The description implies the appropriate use case: comparing 2–10 channels head-to-head on growth and engagement metrics. It does not explicitly name alternatives or state when not to use the tool, but the context is clear enough for an agent to choose it over single-channel tools or niche-discovery tools.

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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