WebMCP workspace
WebMCP
AS
01
WebMCP challengeHuman + agent collaboration
PDF

WebMCP challenge brief

Build a collaborative canvas where a human and an external AI agent share context through WebMCP. Human selections become temporary spoken keywords. The agent resolves those references, reads only the required content, communicates before acting, and leaves visible work on the canvas.

01
WebMCP challenge brief.pdf
Selection to action flow
Selection to action flow
developers.openai.com/showcase?view=webmcp-apps
OpenAI WebMCP apps showcase
MARKDOWN

agent-collaboration-model

# Shared understanding The agent collaborates on the work, not on the webpage. ## Sequence 1. Resolve the human’s keyword. 2. Read the selected item details. 3. Communicate the intended action. 4. Make the change. 5. Report the result using the canonical keyword.

01
agent-collaboration-model.md
CSVwebmcp-tool-evaluation.csv
toolpurposestatus
canvas_listOrient agentReady
canvas_resolveMatch speech to targetsReady
canvas_readRead content and geometryReady
canvas_captureInspect pixelsReady
canvas_createCreate in a regionReady
canvas_updateEdit content and commentsReady
webmcp-tool-evaluation.csv
NOTE

Resolve first. Use the exact keyword. Announce before changing work. Keep the collaboration visible.

Agent protocol
82%
Canvas