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l4b4r4b4b4

YouTube MCP Server

by l4b4r4b4b4

semantic_search_all

Find relevant content across transcripts and comments using natural language queries. Automatically indexes missing content for comprehensive search results.

Instructions

Search across all content types (transcripts and comments).

Performs unified semantic search over both video transcripts and comments. Automatically indexes any missing content before searching.

Args: query: Natural language search query (e.g., "Nix garbage collection"). content_types: List of content types to search: ["transcript", "comment"]. If None, searches all types. channel_ids: Optional list of YouTube channel IDs to scope the search. video_ids: Optional list of specific video IDs to scope the search. k: Number of results to return (default: 10). language: Preferred transcript language code (default: "en"). max_comments_per_video: Maximum comments to index per video (default: 100). max_videos_per_channel: Maximum videos to fetch per channel (default: 50). min_score: Optional minimum similarity score threshold (lower is better).

Returns: Dictionary with search results including: - query: The original search query - results: List of matches with content_type field indicating source - total_results: Number of results returned - indexing_stats: Statistics for both transcripts and comments - content_types_searched: List of content types that were searched

Note: - Results are sorted by relevance score across all content types - Each result includes content_type field ("transcript" or "comment") - Transcript results include timestamp_url, comment results include author

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
kNo
queryYes
languageNoen
min_scoreNo
video_idsNo
channel_idsNo
content_typesNo
max_comments_per_videoNo
max_videos_per_channelNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

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

No annotations provided, so description carries full burden. It discloses the automatic indexing side effect and describes return format. It does not mention permission needs or rate limits, but for a search tool this is acceptable. It could be more explicit about resource usage.

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

Conciseness4/5

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

The description is well-structured with Args and Returns sections and front-loaded purpose. It is longer than necessary but every sentence adds value. Could be slightly more concise.

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 9 parameters and an output schema, the description covers all parameters and explains return structure. It also mentions auto-indexing. It is complete but could include more on sorting behavior or examples.

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 description must add meaning. The Args section provides clear explanations for each parameter (e.g., query as 'Natural language search query', content_types as list of types), which fully compensates for the lack of schema descriptions.

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 'Search across all content types (transcripts and comments)' and 'unified semantic search', using specific verbs and resource. It distinguishes from siblings like semantic_search_transcripts and semantic_search_comments by being the combined version.

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 explains it searches all content types and auto-indexes, and provides parameter examples. However, it lacks explicit guidance on when to use this tool vs the single-type semantic search tools (e.g., 'if you want both, use this; if only one, use the specific one').

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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