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gapup-mcp

by io.github.getgapup

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
NameRequiredDescriptionDefault
urlYesYouTube URL, podcast RSS feed URL, or direct MP3/MP4 URL. Example: "https://www.youtube.com/watch?v=jNQXAC9IVRw"
langNoISO 639-1 language hint (e.g. "en", "fr", "de"). Default "auto".
asyncNoIf 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_maxNoMaximum number of chapters. Default 8.
output_formatNoTranscript format. Default "json".
include_summaryNoInclude extractive summary. Default true.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYes
statusYes
signalsYes
sourcesYes
summaryNo
chaptersYes
segmentsYes
key_topicsYes
transcriptYes
source_typeYes
lang_detectedYes
quality_scoreYes
duration_secondsYes

TDQS

A4.4/5.0
Behavior5/5

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

The description goes far beyond annotations by disclosing technical details: keyless YouTube captions, Whisper API dependency for direct media, TF-IDF fallback for chapters, summary extraction approach, language detection heuristics, SLA of 15s, and 24h cache TTL. No contradiction with the readOnlyHint/idempotentHint annotations.

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 relatively long but well-structured with bullet sections for pipeline, chapters, summary, language detection, and output formats. Each section adds useful operational context. Slightly verbose with the audience list, but appropriate for a complex media-processing tool.

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?

For a tool handling multiple media source types with output formats and async support, the description covers input variations, pipeline behavior, fallbacks, performance limits, and caching. Since an output schema exists, the description does not need to detail return values, and it provides everything else an agent needs for correct invocation.

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% for all 6 parameters with clear descriptions, so baseline is 3. The description does not add meaning to the parameters; it even mentions a 'force_whisper:true' flag not present in the schema, which could confuse an agent. Minimal added value beyond 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?

The description opens with a specific verb and resource: 'Transcription and chapterization of long-form media.' It names concrete source types (YouTube, podcasts, direct audio/video) and target use cases (content marketing, podcast publishers, etc.), making the tool's purpose immediately clear and distinct from siblings.

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 the pipeline for each input type (YouTube, podcast RSS, direct media) and notes when direct media requires extra setup (Whisper API). It gives clear context on when to use the tool, though it does not explicitly name alternatives or exclusions.

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

C2.4/5.0
Disambiguation1/5

Over 50 tools share the identical template 'Gapup agent-payable C-suite expertise' with similar French descriptions and reference cases, making their boundaries indistinguishable. Clusters like competitor_intel, competitive_deep_dive, competitor_moves, competitor_profiles, competitor_pricing_radar, competitor_pricing_scrape, and competitor_recommendations heavily overlap in purpose.

Naming Consistency1/5

Names are chaotic: mix of French and English, snake_case and camelCase, verb_noun, noun, and adjective forms with no uniform pattern. Examples like 'bp_narratif', 'content_enrichment', 'ai_governance_full_report_async', and 'job_result' show no coherent naming convention.

Tool Count1/5

271 tools is far beyond any reasonable MCP server scope, creating an overwhelming selection burden for agents. This count vastly exceeds the 25+ threshold for 'too many' and makes navigation impractical.

Completeness2/5

While the server covers many business domains, it lacks lifecycle operations (e.g., no update/delete tools for the deliverables it generates) and the input specifications are vague ('documented case fields' without documentation), creating functional dead ends. The sheer breadth does not compensate for these gaps.

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