video-research-mcp

Claude Code can't process video or generate media. Gemini 3.1 Pro and ElevenLabs can. This plugin bridges them all -- giving Claude access to video understanding, deep research, web search, and a full media production toolkit through MCP.

What's in the box

A Claude Code plugin with 51 tools, 17 slash commands, 12 skills, and 7 sub-agents. The MCP servers provide the tools. The commands give you quick workflows (/gr:video, /gr:research). The skills teach Claude how to use everything -- from research to cinematic video production. The agents handle background tasks like parallel research and visualization.

Server	Tools	Purpose
video-research-mcp	34	Video analysis, deep research, content extraction, web search, academic papers, knowledge store
video-explainer-mcp	15	Synthesize explainer videos from research (wraps video_explainer)
video-agent-mcp	2	Parallel scene generation via Claude Agent SDK

Install

npx video-research-mcp@latest
export GEMINI_API_KEY="your-key-here"

One command. One API key. The installer copies 17 commands, 12 skills, and 7 agents to ~/.claude/ and configures the MCP servers to run via uvx from PyPI.

npx video-research-mcp@latest --check     # show install status
npx video-research-mcp@latest --uninstall  # clean removal
npx video-research-mcp@latest --local      # install for this project only

Requires Python >= 3.11, uv, Node.js >= 16, and a Google AI API key.

What it does

Watch a meeting recording

/gr:video-chat ~/recordings/project-kickoff.mp4
> "Create meeting minutes in Dutch. Screenshot every shared screen."

Gemini watches the full video and pulls out timestamps, decisions, and action items. For local files, ffmpeg extracts frames at key visual moments. Files over 20MB are uploaded to Gemini's File API and context-cached -- follow-up questions reuse the cache instead of re-uploading.

Analyze a YouTube tutorial

/gr:video https://youtube.com/watch?v=...

Same capabilities, applied to YouTube. Precise timestamps, a concept map, and comment sentiment analyzed in the background.

Research a topic with evidence grading

/gr:research "HNSW index parameters for high-dimensional embeddings"

Runs web search and Gemini analysis in parallel. Every finding gets an evidence tier -- Confirmed, Strong Indicator, Inference, or Speculation -- so you know how much weight to give each claim. Results are visualized as an interactive evidence network.

Analyze papers, URLs, or directories

/gr:analyze https://arxiv.org/abs/2401.12345
/gr:analyze ~/papers/attention-is-all-you-need.pdf
/gr:analyze ~/papers/                              # cross-document comparison

Works with PDFs, URLs, and raw text. Extracts entities, relationships, and key arguments. Point it at a directory and it compares all documents in a single pass. Supports PDF, TXT, MD, HTML, XML, JSON, CSV.

Research grounded in source documents

/gr:research-doc ~/papers/
/gr:research-doc paper1.pdf paper2.pdf "Compare methodologies and find contradictions"

Four-phase pipeline: Document Mapping, Evidence Extraction, Cross-Reference, Synthesis. Every claim is cited back to document and page number.

Search the web

/gr:search "latest developments in MCP protocol"

Google Search via Gemini grounding with source citations.

Recall what you've learned

/gr:recall                                # overview: stats + saved analyses
/gr:recall "kubernetes"                   # semantic search + filesystem grep
/gr:recall ask "what do I know about X?"  # AI-powered Q&A with source citations

Nothing gets lost. Every analysis and research finding is stored automatically. Weeks later, in a different project, you just ask. When Weaviate is configured, searches use semantic matching. Without it, recall falls back to exact keyword grep over saved files.

Use it as a standalone MCP server

The tools are standard MCP. Any MCP client can call them -- no Claude Code required.

{
  "mcpServers": {
    "video-research": {
      "command": "uvx",
      "args": ["video-research-mcp"],
      "env": { "GEMINI_API_KEY": "${GEMINI_API_KEY}" }
    }
  }
}

Commands

Command	What it does
/gr:video <source>	One-shot video analysis with concept map and frame extraction
/gr:video-chat <source>	Multi-turn video Q&A with progressive note-taking
/gr:research <topic>	Deep research with evidence-tier labeling
/gr:research-deep <topic>	Launch Gemini Deep Research Agent with interview-built brief
/gr:research-doc <files>	Evidence-tiered research grounded in source documents
/gr:analyze <content>	Analyze any URL, file, text, or directory of documents
/gr:search <query>	Web search via Gemini grounding
/gr:recall [filter]	Browse past analyses from memory
/gr:models [preset]	Switch Gemini model preset (best/stable/budget)
/gr:getting-started	Guided onboarding and environment check
/gr:ingest <file>	Import external structured knowledge into Weaviate
/gr:traces [filter]	Query, debug, and evaluate MLflow traces
/gr:doctor [quick|full]	Diagnose MCP wiring, API keys, Weaviate, and MLflow connectivity
/gr:advisor <task>	Get workflow advice -- which /gr command fits your task
/ve:explainer <project>	Create and manage explainer video projects
/ve:explain-video <project>	Generate a full explainer video from project content
/ve:explain-status <project>	Check render progress and pipeline state

How a command runs

/gr:video-chat ~/recordings/call.mp4
> "Summarize this meeting, extract action items"

 Phase 1   Gemini analyzes the video
 Phase 2   Results saved to memory
 Phase 2.5 ffmpeg extracts frames (local files only)
 Phase 3   Concepts and relationships enriched
 Phase 4   Interactive visualization generated (opt-in)
 Phase 5   Playwright screenshots it
 Phase 6   Everything copied to output/<slug>/

Visualization and comment analysis run as background agents, so they don't block the conversation.

Output structure

output/project-kickoff-2026-02-28/
├── analysis.md          # timestamped analysis with YAML frontmatter
├── frames/              # extracted video frames (local files)
├── concept-map.html     # interactive visualization
└── screenshot.png       # static capture

Tools

video-research-mcp -- 34 tools

Video (4): video_analyze, video_create_session, video_continue_session, video_batch_analyze

YouTube (3): video_metadata, video_comments, video_playlist

Research (4): research_deep, research_plan, research_assess_evidence, research_document

Deep Research Agent (4): research_web, research_web_status, research_web_followup, research_web_cancel

Academic Research (5): research_paper_search, research_paper_details, research_paper_citations, research_paper_recommendations, research_author_search

Content (3): content_analyze, content_batch_analyze, content_extract

Search (1): web_search

Infrastructure (2): infra_cache, infra_configure

Knowledge (8): knowledge_search, knowledge_related, knowledge_stats, knowledge_fetch, knowledge_ingest, knowledge_schema, knowledge_ask, knowledge_query

video-explainer-mcp -- 15 tools

Project (4): explainer_create, explainer_inject, explainer_status, explainer_list

Pipeline (6): explainer_generate, explainer_step, explainer_render, explainer_render_start, explainer_render_poll, explainer_short

Quality (3): explainer_refine, explainer_feedback, explainer_factcheck

Audio (2): explainer_sound, explainer_music

video-agent-mcp -- 2 tools

Scene Generation (2): agent_generate_scenes, agent_generate_single_scene

Skills

Skills teach Claude how to use tools and workflows correctly. They load automatically when relevant -- you never invoke them manually.

Research & analysis

Skill	What it teaches
video-research	All 34 video-research-mcp tools -- selection, caching, error handling
video-explainer	The 15 explainer tools -- pipeline order, rendering, TTS config
gemini-visualize	Interactive HTML visualizations from analysis results
weaviate-setup	Guided Weaviate onboarding and connection setup
mlflow-traces	MLflow trace querying, debugging, and evaluation
research-brief-builder	Structured research briefs for deep research
gr-advisor	Recommends the right /gr command for your task

Media production (new in v0.6.0)

Skill	What it teaches
tts-production	ElevenLabs TTS -- API patterns, voice presets, cosine-ease ducking, multilingual narration
ffmpeg-production	Post-processing chain order, codec selection, platform export presets
video-generation	AI video with Veo or Sora -- provider selection matrix, draft-to-final workflow
video-production	Cinematic multi-shot -- style anchors, 4 chaining patterns, frame-level QA
image-generation	Style anchor prompt optimization for mcp-image (Subject-Context-Style)

The production skills use progressive disclosure: core patterns load when triggered (~1,000 words each), detailed recipes and reference tables live in references/ and load on demand.

Knowledge store

Connect Weaviate, and everything you learn gets stored -- searchable across projects, across sessions. Without it, the plugin works the same; you just don't get persistent semantic search.

Thirteen collections are created on first connection:

Collection	Filled by
ResearchFindings	research_deep, research_assess_evidence, research_document
VideoAnalyses	video_analyze, video_batch_analyze
ContentAnalyses	content_analyze, content_batch_analyze
VideoMetadata	video_metadata
SessionTranscripts	video_continue_session
WebSearchResults	web_search
ResearchPlans	research_plan
DeepResearchReports	research_web_status, research_web_followup
CommunityReactions	comment analysis (via /gr:video agent)
ConceptKnowledge	concept extraction from analyses
RelationshipEdges	relationship mapping between concepts
CallNotes	meeting/call analysis notes
AcademicPapers	research_paper_search, research_paper_details, research_paper_citations, research_paper_recommendations

Knowledge graph

Analysis tools (content_analyze, video_analyze, research_deep, research_web, research_document, content_batch_analyze) automatically extract concepts and relationships. Over time this builds a queryable knowledge graph across all your research -- no manual step required.

Eight knowledge tools query this data: hybrid search with optional Cohere reranking, semantic similarity, fetch by UUID, manual ingest, schema introspection, and collection stats. knowledge_ask uses Weaviate's QueryAgent for AI-generated answers with source citations.

# install QueryAgent support
uv pip install 'video-research-mcp[agents]'

To set up Weaviate:

export WEAVIATE_URL="https://your-cluster.weaviate.network"
export WEAVIATE_API_KEY="your-key"

Configuration

Variable	Default	What it does
GEMINI_API_KEY	(required)	Google AI API key
GEMINI_MODEL	gemini-3.1-pro-preview	Primary model
GEMINI_FLASH_MODEL	gemini-3-flash-preview	Fast model for search and summaries
DEEP_RESEARCH_AGENT	deep-research-pro-preview-12-2025	Interactions API agent
WEAVIATE_URL	""	Weaviate URL (empty = knowledge store disabled)
WEAVIATE_API_KEY	""	Required for Weaviate Cloud
COHERE_API_KEY	""	Enables Cohere reranker in knowledge search
ELEVENLABS_API_KEY	""	For TTS voice-over production
OPENAI_API_KEY	""	For Sora video generation and OpenAI TTS
YOUTUBE_API_KEY	""	YouTube Data API key (falls back to GEMINI_API_KEY)
S2_API_KEY	""	Semantic Scholar API key (higher rate limits)
MLFLOW_TRACKING_URI	""	MLflow server URL (empty = tracing disabled)
EXPLAINER_PATH	""	Path to cloned video_explainer repo
EXPLAINER_TTS_PROVIDER	"mock"	TTS provider: mock, elevenlabs, openai, gemini, edge

All configuration variables

Variable	Default	What it does
GEMINI_THINKING_LEVEL	high	Thinking depth (minimal / low / medium / high)
GEMINI_TEMPERATURE	1.0	Sampling temperature
GEMINI_CACHE_DIR	~/.cache/video-research-mcp/	Cache directory
GEMINI_CACHE_TTL_DAYS	30	Cache expiry
GEMINI_MAX_SESSIONS	50	Max concurrent video sessions
GEMINI_SESSION_TIMEOUT_HOURS	2	Session TTL
GEMINI_SESSION_MAX_TURNS	24	Max turns per session
GEMINI_SESSION_DB	""	SQLite path for session persistence (empty = in-memory)
MLFLOW_EXPERIMENT_NAME	video-research-mcp	MLflow experiment name

Other install methods

Standalone MCP server (no plugin assets)

{
  "mcpServers": {
    "video-research": {
      "command": "uvx",
      "args": ["video-research-mcp"],
      "env": { "GEMINI_API_KEY": "${GEMINI_API_KEY}" }
    }
  }
}

Claude Desktop

Add to claude_desktop_config.json:

{
  "mcpServers": {
    "video-research": {
      "command": "uvx",
      "args": ["video-research-mcp"],
      "env": { "GEMINI_API_KEY": "your-key-here" }
    }
  }
}

From source

git clone https://github.com/Galbaz1/video-research-mcp
cd video-research-mcp
uv venv && source .venv/bin/activate && uv pip install -e ".[dev]"
node bin/install.js --global

Development

uv venv && source .venv/bin/activate
uv pip install -e ".[dev]"
uv run pytest tests/ -v        # 781 tests, all mocked
uv run ruff check src/ tests/  # lint

Troubleshooting

Problem	Fix
No API key error	Set GEMINI_API_KEY
429 / quota exceeded	Wait 60s, or switch to /gr:models budget for higher rate limits
Video analysis empty	Video may be private, age-restricted, or region-locked
No frames extracted	Install ffmpeg: brew install ffmpeg
Visualization missing	Ensure Node.js is on PATH (Playwright runs via npx)
Weaviate won't connect	Check WEAVIATE_URL and that the instance is running
Knowledge tools empty	Set WEAVIATE_URL to enable the knowledge store
weaviate-agents not installed	uv pip install 'video-research-mcp[agents]'
MLflow tools unavailable	Set MLFLOW_TRACKING_URI and start mlflow server --port 5001
ElevenLabs MCP tools 404	Use curl API calls instead -- see tts-production skill

Contributing

See CONTRIBUTING.md for development setup and PR guidelines. See ROADMAP.md for planned work. Report security issues via SECURITY.md.

Author

Fausto Albers -- Lead Gen AI Research & Development at the Industrial Digital Twins Lab, Amsterdam University of Applied Sciences (HvA), in the research group of Jurjen Helmus. Founder of Wonder Why.

Credits

• Google Gemini (google-genai SDK) -- Gemini 3.1 Pro provides native video understanding, thinking mode, context caching, and the 1M token window that makes all of this work.
• FastMCP -- MCP server framework. The composable sub-server pattern (app.mount()) keeps 51 tools organized across 3 servers.
• Weaviate -- vector database powering the knowledge store. Thirteen collections, hybrid search, and the Weaviate Claude Code skill that inspired the knowledge architecture.
• ElevenLabs -- text-to-speech with word-level timestamps for voiceover generation.
• video_explainer by prajwal-y -- the video synthesis engine behind the explainer pipeline.
• Pydantic -- schema validation for all tool I/O. Structured generation via model_json_schema().
• MLflow (mlflow-tracing) -- optional observability. Every Gemini call becomes a traceable span with token counts and latency.
• Cohere -- optional reranking in knowledge search for better result relevance.
• Remotion -- React-based video rendering for the explainer pipeline.
• Claude Agent SDK -- powers parallel scene generation in video-agent-mcp.

License

MIT