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l4b4r4b4b4

YouTube MCP Server

by l4b4r4b4b4
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Python
Remote

index_channel_transcripts

Pre-index all video transcripts from a YouTube channel to speed up semantic searches. Use this optional tool to prepare channel content for faster first searches.

Instructions

Pre-index all video transcripts from a YouTube channel.

This is an optional tool for pre-warming the semantic search index. You don't need to call this before searching - semantic_search_transcripts automatically indexes missing videos. Use this only if you want to explicitly prepare a channel's content for faster first searches.

Args: channel_id: YouTube channel ID (e.g., "UCuAXFkgsw1L7xaCfnd5JJOw"). max_videos: Maximum number of videos to index (default: 50). language: Preferred transcript language code (default: "en"). force_reindex: If True, re-index videos even if already indexed.

Returns: Dictionary with indexing results: - indexed_count: Number of videos successfully indexed - chunk_count: Total chunks created - skipped_count: Videos skipped (already indexed or no transcript) - error_count: Number of failed videos - errors: List of error messages - video_ids: List of indexed video IDs

Note: - Indexing 50 videos takes ~1-2 minutes - Uses ~1 API quota unit per video (transcripts are free) - Subsequent semantic searches on this channel will be fast

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
languageNoen
channel_idYes
max_videosNo
force_reindexNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Tool Definition Quality

A5/5.0
Behavior5/5

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

No annotations provided, but description discloses time estimates (1-2 min for 50 videos), API quota usage (1 unit per video), and benefits (fast subsequent searches). Also details return structure.

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?

Well-structured with clear sections (Args, Returns, Note). Front-loaded with purpose. No extraneous information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity and the presence of an output schema, the description covers all essential aspects: purpose, usage context, parameters, return values, behavioral notes, and limitations.

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?

Despite 0% schema coverage, the description includes a docstring with explanations for each parameter (channel_id, max_videos, language, force_reindex) including examples and defaults, adding meaning beyond the schema.

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?

Description clearly states it pre-indexes video transcripts from a YouTube channel for pre-warming the semantic search index. It distinguishes itself from sibling tools like semantic_search_transcripts by noting it's optional and for faster first searches.

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

Usage Guidelines5/5

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

Explicitly states when to use (pre-warming) and when not to use (not needed before searching). References alternative (semantic_search_transcripts) and provides context for faster searches.

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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