gapup-mcp

transcribe_chapterize_media

Read-onlyIdempotent

Transcription and chapterization of long-form media (YouTube, podcasts, direct audio/video) for content marketing teams, podcast publishers, edu tech, journalists and accessibility/compliance.

Pipeline: • YouTube → timedtext captions (keyless) + oEmbed metadata + native timecode chapters from description • Podcast RSS → episode description + duration + timecodes if embedded in show notes • Direct media → partial (requires Whisper API via OPENAI_API_KEY + force_whisper:true) • Chapters: native YouTube timecodes preferred; heuristic TF-IDF segmentation as fallback • Summary: extractive TF-IDF top-sentences (no LLM required) • Language detection: character-set heuristic (CJK→zh, kana→ja, hangul→ko, accents→fr/de/es)

Output formats: json (full structured object) | text (plain transcript) | srt | vtt

SLA: ≤15s budget total. Cache: 24h TTL.

Input Schema

TableJSON Schema

Name	Required	Description
`url`	Yes	YouTube URL, podcast RSS feed URL, or direct MP3/MP4 URL. Example: "https://www.youtube.com/watch?v=jNQXAC9IVRw"
`lang`	No	ISO 639-1 language hint (e.g. "en", "fr", "de"). Default "auto".
`async`	No	If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.
`chapters_max`	No	Maximum number of chapters. Default 8.
`output_format`	No	Transcript format. Default "json".
`include_summary`	No	Include extractive summary. Default true.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`url`	Yes
`status`	Yes
`signals`	Yes
`sources`	Yes
`summary`	No
`chapters`	Yes
`segments`	Yes
`key_topics`	Yes
`transcript`	Yes
`source_type`	Yes
`lang_detected`	Yes
`quality_score`	Yes
`duration_seconds`	Yes

TDQS

A4.6/5.0

Behavior5/5

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

Annotations already indicate readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds significant behavioral context: pipeline steps (YouTube keyless captions, podcast oEmbed, direct Whisper API), chapterization fallback, summary method, language detection heuristics, output formats, SLA, and caching. This exceeds what annotations provide.

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 detailed and well-structured with bullet points for the pipeline steps. It is informative without being overly verbose, though some sentences could be trimmed for further conciseness.

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 (6 parameters, multiple sources, and an output schema), the description covers all essential aspects: sources, pipeline, parameters, SLA, caching, and output formats. The output schema exists, so return values do not need to be described.

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?

Input schema has 100% description coverage, so baseline is 3. The description adds value by explaining how parameters like async, chapters_max, output_format, and include_summary affect the processing pipeline, providing context beyond the schema definitions.

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 performs transcription and chapterization of long-form media from YouTube, podcasts, and direct audio/video. It lists specific supported sources and the pipeline, distinguishing it from siblings by its scope and capabilities.

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 provides guidance on when to use the tool for different media sources (YouTube, podcast RSS, direct media) and explains the chapterization preference (native timecodes first, then heuristic). It mentions SLA and caching but does not explicitly state when not to use the tool or list alternatives among siblings.

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

All gapup-mcp tools Inspect

Try in Browser

RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

C2.5/5.0

Disambiguation2/5

With 271 tools, many have overlapping purposes (e.g., multiple competitor intel tools, multiple financial modelers, multiple ESG auditors). Detailed descriptions help slightly, but the sheer volume creates confusion. Agents would struggle to select the right tool among many similar options.

Naming Consistency1/5

Tool names are wildly inconsistent: mix of English and French, snake_case and short phrases, some very generic (process, run, execute equivalents). No discernible naming convention (e.g., abm_architect vs. boundary_control vs. bp_narratif). This makes it hard to predict tool names.

Tool Count1/5

271 tools is far beyond typical well-scoped servers (3-15). This indicates an unfocused, over-bloated tool surface. Even for a general business intelligence server, this number is excessive and violates the principle of each tool earning its place.

Completeness2/5

Despite the large count, coverage feels scattered. Some domains (e.g., content, competitive intel) have many tools, while others (e.g., supply chain, HR) have gaps. The set lacks a coherent scope; it seems like a dump of many separate tool collections rather than a complete, curated surface.

Resources

Need Help?