Skip to main content
Glama

youtube_audio_transcript

Speech-to-text for YouTube audio. Use it when a video has no captions — or when you want a transcript of what was actually spoken rather than YouTube's published captions. Priced per started minute of audio. Costs 2 credits/min of audio. Empty results and failures are never charged. Pass cache=true for a free 24h cache hit (default always fresh).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYesPublic YouTube video URL, e.g. https://youtube.com/watch?v=ID. Not a TikTok/Instagram/Facebook URL. The URL platform must match this endpoint's platform. Do not pass cross-platform URLs, e.g. YouTube to TikTok, Instagram to Facebook, LinkedIn to X/Twitter, or Pinterest to Rumble.
cacheNoSet true to serve from the 24h response cache (0 credits on hit). Default false — always fetch fresh.
languageNoPreferred caption language as an ISO code, e.g. "en". Defaults to auto-detect.
maxCreditsNoRefuse before STT when estimatedCredits would exceed this (400 cost_exceeds_max, 0 credits). The estimate is on every success and, when the extract miss is retryable, as estimatedCreditsIfRetried.

TDQS

A4.4/5.0
Behavior4/5

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 covers pricing ('2 credits/min of audio'), failure handling ('Empty results and failures are never charged'), and cache behavior ('cache=true for a free 24h cache hit, default always fresh'). It does not describe the output format, but the operational cost and failure semantics are well disclosed.

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 two concise sentences that front-load the core purpose, then pack pricing, failure policy, and cache behavior into efficient clauses. Every sentence earns its place; no redundant or filler content.

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 absence of annotations and output schema, the description does well by covering cost, failure charging, cache behavior, and when to use the tool. It leaves the exact return format unstated, but the name and 'transcript' purpose make the output reasonably predictable. The rich input schema compensates for the remaining gaps.

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

Parameters4/5

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

Schema description coverage is 100%, so the baseline is 3. The description adds valuable pricing context ('2 credits/min') that directly informs the maxCredits parameter, and clarifies the cache parameter's practical benefit. While cache details already exist in the schema, the credit-cost framing goes beyond the schema's own text.

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 'Speech-to-text for YouTube audio', a specific verb plus resource, and clearly differentiates itself from caption-based tools by noting it captures what was actually spoken rather than YouTube's published captions. This makes it distinguishable from sibling tools like youtube_transcript and video_transcript without needing to inspect their schemas.

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 gives explicit when-to-use guidance: 'Use it when a video has no captions — or when you want a transcript of what was actually spoken rather than YouTube's published captions.' It clearly implies an alternative caption-based path, but it does not name the specific sibling tool, so the exclusion is slightly less direct than ideal.

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

Try in Browser

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

B3.2/5.0
Disambiguation3/5

The platform-prefix convention keeps most of the 178 tools clearly separated, but several clusters are genuinely ambiguous: tiktok_live_info is explicitly described as 'Identical to TikTok Live', instagram_basic_profile and instagram_channel_details both return profile stats, and facebook_profile_posts overlaps with facebook_profile_reels. The generic 'Summarizer' descriptions for facebook_summarize, instagram_summarize, and tiktok_summarize provide no disambiguating detail at all.

Naming Consistency4/5

The dominant snake_case platform_resource_suffix pattern is followed remarkably consistently across 178 tools (e.g. youtube_channel_videos, tiktok_search_users, reddit_subreddit_posts). Minor deviations exist: the same creator resource is called 'channel' in some tools (tiktok_channel_details, instagram_channel_posts) but 'profile' or 'user' in others (facebook_profile_posts, twitch_user_videos, linnkme_profile); link-in-bio tools mostly use _page but linkme uses _profile; and the video_summarize/video_transcript pair lacks a platform prefix.

Tool Count2/5

At 178 tools this is far beyond what any agent can efficiently navigate in a single flat namespace, and even individual platform subsets exceed reasonable bounds (TikTok alone has ~34 tools, YouTube ~25). The sheer breadth of the multi-platform scope partially justifies the count, but the server would be far more usable split into per-platform servers.

Completeness4/5

The read-only data surface is impressively thorough: nearly every platform has profile + content + search + comments coverage, and TikTok, YouTube, Instagram, and Facebook are covered end-to-end including shops, ads, transcripts, and summaries. Notable gaps are minor: Twitter has no keyword search tool, LinkedIn lacks comments, and Reddit has no user-profile endpoint, but none of these create dead ends for the server's core data-retrieval purpose.