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Glama

Youtube

by io.github.pipeworx-io

Yt Video Comments

yt_video_comments
Read-onlyIdempotent

Get top-level comment threads on a YouTube video. Returns author, text, like count, and publish date.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
orderNoSort order: "time" or "relevance" (default: "relevance")
_apiKeyYesYouTube Data API v3 key from Google Cloud Console
video_idYesVideo ID to fetch comments for
max_resultsNoNumber of comment threads to return (default 20, max 100)

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
commentsYesArray of top-level comment thread objects
total_resultsYesTotal number of comment threads

Schema Changelog

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

  1. Changed1 schema field changed
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
+  "properties": {
+    "comments": {
+      "description": "Array of top-level comment thread objects",
+      "items": {
+        "properties": {
+          "author": {
+            "description": "Author display name",
+            "type": "string"
+          },
+          "author_channel_url": {
+            "description": "Author's channel URL",
+            "type": "string"
+          },
+          "comment_id": {
+            "description": "Comment ID",
+            "type": "string"
+          },
+          "like_count": {
+            "description": "Number of likes on comment",
+            "type": "number"
+          },
+          "published_at": {
+            "description": "ISO 8601 publish date",
+            "type": "string"
+          },
+          "reply_count": {
+            "description": "Number of replies to comment",
+            "type": "number"
+          },
+          "text": {
+            "description": "Comment text content",
+            "type": "string"
+          },
+          "updated_at": {
+            "description": "ISO 8601 last update date",
+            "type": "string"
+          }
+        },
+        "required": [
+          "comment_id",
+          "author",
+          "author_channel_url",
+          "text",
+          "like_count",
+          "reply_count",
+          "published_at",
+          "updated_at"
+        ],
+        "type": "object"
+      },
+      "type": "array"
+    },
+    "total_results": {
+      "description": "Total number of comment threads",
+      "type": "number"
+    }
+  },
+  "required": [
+    "total_results",
+    "comments"
+  ],
+  "type": "object"
+}
  2. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "_apiKey": "your-youtube-api-key",
+    "video_id": "dQw4w9WgXcQ"
+  },
+  {
+    "_apiKey": "your-youtube-api-key",
+    "max_results": 50,
+    "order": "time",
+    "video_id": "dQw4w9WgXcQ"
+  }
+]
  3. First observed

TDQS

A3.8/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and non-destructive. The description adds value by specifying the returned fields (author, text, like count, publish date). However, it does not mention pagination behavior or potential rate limits.

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 exceptionally concise—two sentences with no unnecessary words. It is front-loaded with the core action and return information.

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?

Given the tool's simplicity, rich annotations, complete schema, and existing output schema, the description covers the essentials. However, it omits the fact that only top-level threads are returned (not replies), which could be useful context.

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 coverage is 100% with full parameter descriptions. The description does not add meaning beyond what the schema already provides, so baseline 3 is appropriate.

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 clearly states the tool retrieves top-level comment threads on a YouTube video, specifying the return fields. It distinguishes itself from sibling tools like yt_video_details and yt_search, which perform different functions.

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?

No guidance is provided on when to use this tool versus alternatives, such as for fetching replies or handling pagination. The context of top-level only is implied but not explicitly stated.

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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TDQS

A3.6/5.0
Disambiguation3/5

There is meaningful overlap among the ask_pipeworx, deep_research, validate_claim, and polymarket_* tools, but the descriptions do draw fairly clear boundaries between them. The five yt_* tools are distinct and easy to tell apart, though the unrelated Pipeworx cluster makes the overall set feel muddier than it should.

Naming Consistency3/5

Most tools use readable snake_case, and there are coherent prefixes like yt_ and polymarket_, but the set mixes bare verbs (remember, recall, forget, subscribe), noun-style names (entity_profile, deep_research), and API-like names (ask_pipeworx, generate_llms_txt). The pattern is not chaotic, but it is inconsistent across the set.

Tool Count2/5

36 tools is well above the typical well-scoped range, and the vast majority are unrelated to the server's stated 'Youtube' identity. The actual YouTube surface is only five tools, while 31 tools belong to a different Pipeworx/Polymarket domain.

Completeness2/5

For a YouTube-focused server, the yt_* tools cover search, channel info, video details, and comments, but miss obvious surfaces like playlists, transcripts, subscriptions, uploads, and video updates. The large non-YouTube tool collection does not fill these gaps; it only makes the server feel mis-scoped.

Resources