video-explainer

name	video-explainer
description	Teaches Claude how to use the 15 video explainer tools to create explainer videos from research content. Activates when working with video synthesis, explainer creation, or the video-explainer MCP server.

Video Explainer MCP — Tool Usage Guide

You have access to the video-explainer-mcp MCP server, which wraps the video_explainer CLI to synthesize explainer videos from text content.

Core Concept

This server is a synthesis companion to video-research-mcp. Research extracts knowledge; this server turns it into video. The pipeline is: content → script → narration → scenes → voiceover → storyboard → render.

Tool Selection Guide

I want to...	Use this tool
Create a new video project	explainer_create
Feed content into a project	explainer_inject
Check project progress	explainer_status
List all projects	explainer_list
Run the full pipeline	explainer_generate
Run one pipeline step	explainer_step
Preview render (blocking)	explainer_render
Start background render	explainer_render_start
Check render progress	explainer_render_poll
Generate short-form video	explainer_short
Improve a step's output	explainer_refine
Add iterative feedback	explainer_feedback
Verify script accuracy	explainer_factcheck
Add sound effects	explainer_sound
Add background music	explainer_music

Pipeline Order

Steps must run in order. Each step depends on the previous step's output:

1. script      — Generate video script from input content
2. narration   — Convert script to narration text
3. scenes      — Generate scene descriptions
4. voiceover   — Synthesize speech audio (TTS)
5. storyboard  — Create visual storyboard
6. render      — Combine into final video

Use explainer_generate to run all steps, or explainer_step for one at a time.

Content Injection

Before running the pipeline, inject content:

explainer_create(project_id="quantum-computing")
explainer_inject(
    project_id="quantum-computing",
    content="# Quantum Computing\n\nKey concepts:\n- Superposition...",
    filename="research.md"
)

Content can be:

• Research output from research_deep
• Video analysis from video_analyze
• Any markdown or plain text

Background Render Pattern

For long renders (1080p, 4K), use the start/poll pattern:

# Start render in background
result = explainer_render_start(project_id="my-video", resolution="1080p", fast=False)
job_id = result["job_id"]

# Poll every 30 seconds
status = explainer_render_poll(job_id=job_id)
# status["status"] is "pending", "running", "completed", or "failed"

Quality Iteration Loop

After generating the pipeline:

1. explainer_factcheck(project_id) — Verify claims
2. explainer_feedback(project_id, "Make the intro more engaging") — Add notes
3. explainer_refine(project_id, phase="script") — Improve specific phase
4. Re-run dependent steps: explainer_generate(project_id, from_step="narration")

TTS Provider Selection

Provider	Quality	Cost	Timestamps	Status
mock	None	Free	N/A	Default — for testing
elevenlabs	Excellent	$165-330/1M chars	Native	Recommended
openai	Good	$15/1M chars	Whisper	Budget alternative
gemini	Good	~$16/1M chars	Whisper	Experimental
edge	Variable	Free	Native	Deprecated — auth issues

Set via EXPLAINER_TTS_PROVIDER in ~/.config/video-research-mcp/.env.

ElevenLabs Voice Settings

When using elevenlabs as TTS provider, configure voice characteristics via env vars:

Variable	Range	Default	Effect
ELEVENLABS_VOICE_ID	voice ID string	Rachel	Which voice to use
ELEVENLABS_STABILITY	0.0-1.0	0.45	Lower = more expressive, higher = more consistent
ELEVENLABS_SIMILARITY_BOOST	0.0-1.0	0.75	How closely to match the reference voice
ELEVENLABS_SPEED	0.7-1.2	1.0	Speech pacing (0.7 = slow, 1.2 = fast)

These can also be set per-project in config.yaml under tts:.

Recommended settings for narration: stability=0.45, similarity=0.75, speed=1.0 (natural pacing with emotional range).

Production Order Documents

For complex videos, create a POD (Production Order Document) first — a structured blueprint with script, storyboard, audio direction, and visual specs. PODs live in docs/plans/ and can be used as input via /ve:explainer → "From a Production Order".

Error Handling

All tools return error dicts on failure:

{
  "error": "description",
  "category": "SUBPROCESS_FAILED",
  "hint": "actionable fix",
  "retryable": false
}

Common categories:

• EXPLAINER_NOT_FOUND — Set EXPLAINER_PATH
• PROJECT_NOT_FOUND — Check project ID with explainer_list
• NODE_NOT_FOUND — Install Node.js 20+
• FFMPEG_NOT_FOUND — Install FFmpeg
• TTS_FAILED — Check TTS provider and API key
• RENDER_FAILED — Check Remotion installation