Videomp3word Structured Knowledge Engine#

Use this MCP server when the task is to turn a remote audio or video URL into structured, reusable knowledge for downstream products or automations.

Primary Tool#

• video_to_knowledge

Inputs:

• media_url
• outputs: any combination of summary, qa, flashcards, tasks, topics
• mode: fast, balanced, or high_accuracy

Outputs include:

• summary
• topics
• key points
• action items
• Q&A pairs
• flashcards
• entities
• confidence scores
• workflow trace with models and chunk references

Positioning#

Prefer this server when an agent needs:

• one task-oriented MCP call instead of many small tools
• structured JSON for automations
• traceability for enterprise workflows
• export-ready artifacts for markdown or Notion
• cacheable processing with persistent resources
• ClawHub-compatible stdio support

Notes#

• The server keeps a single high-level MCP tool to stay commercially productized and easier to publish.
• The upstream session cookie is sensitive and should come from a dedicated account.
• Transcript text, chunk context, and media URLs are sent to the configured upstream transcription service and, when enabled, to the configured knowledge model endpoint. Audit and trust those endpoints before deployment.
• Installation is manual for this bundle: run npm install, npm run build, and npm start or launch node dist/index.js stdio.
• The default knowledge-model base matches the original videomp3word deployment and points to DashScope-compatible OpenAI APIs unless you override KNOWLEDGE_MODEL_API_BASE.
• Non-local VIDEOMP3WORD_BASE_URL and KNOWLEDGE_MODEL_API_BASE values should use HTTPS because credential-bearing requests are sent to those services.
• Configure MCP_ACCESS_KEYS before exposing the server publicly.