This is a submission for the Notion MCP Challenge
What I Built
Every engineering team has the same graveyard: folders full of blurry whiteboard photos that were supposed to become
documentation. They never do. The meeting ends, the momentum dies, and that brilliant architecture sketch slowly rots in
someone's camera roll.
Trace AI kills that problem dead.
Trace is an autonomous pipeline that watches your Notion Design Inbox, your Slack workspace, and your Discord server
simultaneously. The moment you drop in a whiteboard photo, Trace wakes up, reasons through the sketch using Claude vision,
and uses the official Notion MCP server to build a complete, structured system design document — entirely on its own.
Not a summary. Not a description. A full engineering document:
- Mermaid.js architecture diagrams — flowchart and sequence, auto-generated from your ink
- Component breakdown — every service, database, and load balancer identified and described
- Security analysis — potential vulnerabilities flagged with recommendations
- Bottleneck detection — performance risks spotted before they hit production
- Actionable task cards — your handwritten To-Dos extracted and created as real entries in your Notion engineering board, with Priority, Category, and Status pre-filled
- AWS cost estimates — rough monthly infrastructure projections for every component
- Complexity scoring — team size and build timeline estimates
The entire process takes under 2 minutes from photo to polished doc.
In a large company, information decays. A project manager marks a project as "completed", but the task database still has 5 open items. A budget page says '$10k', but the invoice page says '$12k'. Usually, these contradictions stay hidden until something breaks.
Sentinel fixes this. Using the new Model Context Protocol (MCP), Sentinel acts as a "shared brain" across your entire workspace. It observes every edit and cross-references it against a set of global truths you define. If a contradiction occurs, Sentinel doesn't just watch—it acts.
Video Demo
Show us the code
https://github.com/Boweii22/Arch-Vision
Deployed live on Render using Docker (nikolaik/python-nodejs for Python + Node.js in one container — required to run the MCP
server subprocess).
How I Used Notion MCP
This is where Trace AI goes beyond a simple API wrapper.
Trace spawns the official @notionhq/notion-mcp-server as a live subprocess using the MCP Python SDK. Claude then enters an
agentic tool-use loop — it doesn't receive a rigid set of instructions and execute them blindly. It reasons about what
blocks to append, how to structure the content, and how to handle edge cases like long Mermaid diagrams that exceed Notion's
2000-character rich text limit.
Whiteboard photo
↓
Claude Vision → Architecture Analysis (structured JSON)
↓
npx @notionhq/notion-mcp-server ←→ Claude agentic loop
↓ ↓
Notion Page created MCP tool calls:
(direct API, correct parent) - append_block_children
- create_database_page
- (up to 25 iterations)
↓
Tasks Database ← action items extracted
Projects DB ← relational mapping
MCP unlocks something that raw API calls can't: judgment. Claude decides when to split a code block, how to format a
callout, when to skip a section because the data isn't there. The document isn't templated — it's reasoned.
On top of that, Trace runs a bi-directional sync loop: if you manually edit the Mermaid diagram in Notion, Trace detects the change, re-analyzes the modified architecture, and updates the analysis sections to stay consistent with your edits. Your
Notion workspace stays alive, not static.
Three ways to trigger it — Notion Inbox, Slack, Discord — because great tools meet teams where they already live.
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