YouTube Studio MCP

Overview Schema Source Related Servers Discussions Admin

List comments

list_comments

Read-onlyIdempotent

Read comment threads on a channel or single video to summarize audience sentiment, find viewer questions, or spot content ideas.

Instructions

Read comment threads (top-level comments with reply counts) for the channel or one video, e.g. to summarise audience sentiment, find questions, or spot content ideas.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "list_comments"
  }
}

Input Schema

Name	Required	Description	Default
`limit`	No	Maximum comment threads to return.
`order`	No	Newest first, or YouTube's relevance.	time
`save_to`	No	Optional file to also save the rows to (.csv or .json). A bare filename goes in the output directory; absolute paths are allowed.
`video_id`	No	Comments on one video. Omit for recent comments across the channel.
`search_terms`	No	Only threads containing this text.
`include_replies`	No	Include up to 5 replies per thread.
`moderation_status`	No	'heldForReview' / 'likelySpam' show the comments waiting in Studio's review queue.	published

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Nov 11, 2026

TDQS

A4/5.0

Behavior3/5

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

Annotations already declare this a safe, idempotent, read-only, open-world operation, so the safety profile is covered. The description adds the useful structural fact that results are top-level threads carrying reply counts, but says nothing about pagination behavior, quota cost, or what an empty result means — gaps that matter for a networked read against the YouTube API.

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?

A single sentence with the operation and scope front-loaded and the use cases compressed into a short e.g. clause. Nothing is redundant and nothing needs to be re-read.

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 7 optional, fully-documented parameters and an output schema, the definition covers what the agent needs to call it correctly. Only quota/rate-limit and pagination context is missing, which is a minor omission for this complexity level.

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?

Schema description coverage is 100% and every parameter, including the enums for order and moderation_status, is documented in the schema itself. The description adds no parameter-level meaning beyond it, so the baseline 3 applies.

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?

States a specific verb and resource ("Read comment threads") and precisely bounds scope: channel-wide or a single video, top-level threads with reply counts. No sibling in the catalog deals with comments, so there is no ambiguity about which tool retrieves comment data.

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?

Gives concrete motivating scenarios (summarise audience sentiment, find questions, spot content ideas) that tell the agent when this tool is the right pick. It stops short of exclusions or pointing at a related analytics tool for sentiment metrics, so it is clear context without explicit when-not guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.