How external agents drive and consume Agentable through the Model Context Protocol.
External agents interact with Agentable through a focused set of MCP tools. These tools expose the full lifecycle of panels while respecting human-in-the-loop boundaries.
Discover available panels in the current workspace.
Open an existing panel on the canvas.
Populate a panel with structured data.
Create new panels at runtime using validated specs.
Apply targeted updates to existing panels.
Trigger actions defined within a panel.
Retrieve the full schema and current state of a panel.
Every tool belongs to one of four capability classes that define the scope of agent permissions.
View panel state and canvas content without modification.
Open, close, and navigate panel interfaces.
Modify panel data through human-approved patches.
Execute long-running panel actions and workflows.
Agentable provides built-in primitives for safe multi-agent operation on a shared canvas.
Time-bound exclusive access to panels and canvas regions.
Every change is attributed to the originating agent with full audit history.
Separate human-in-the-loop approval queues for each agent to prevent conflicts.
The entire workspace is exposed as an MCP resource. External clients connect under strict scope enforcement, allowing agents to read and act on the canvas while respecting all governance rules defined by the host.
The framework-native registry remains the source of truth. All panel schemas, capabilities, and permissions are validated server-side before any MCP operation is executed.
CopilotKit can be used as an optional transport layer for agent communication. However, the framework-native registry remains the single source of truth for all panel definitions and capabilities.
Agents interact with the system using standard MCP tool call format.
{
"name": "compose_panel",
"arguments": {
"type": "career-profile",
"position": { "x": 420, "y": 180 },
"data": {
"name": "Alex Rivera",
"role": "Senior Product Designer"
}
}
}
Agents can draw and annotate directly on the canvas using read_canvas and screenshot_canvas operations. These high-level primitives allow agents to understand spatial layout and visual context without needing low-level pixel access.
All drawing operations flow through the same governed panel system, ensuring consistency with human-authored content.