PromptBoard by Pjueon serves as a local Model Context Protocol server offering a shared whiteboard for AI agents. The app accepts screenshots, diagrams, and annotations that MCP-compatible agents can access, and includes drawing tools, geometric shapes, text boxes, plus persistent storage and theme options. It integrates with Claude Desktop, Claude Code, and Gemini CLI, runs locally under Node.js, and has an open-source codebase on GitHub. Designed for developers, researchers, and power users who need to add visual context to agent-driven workflows.

What tasks can you actually use it for?

The tool functions as a visual layer for agent conversations, suited to cases where text alone is insufficient. It is practical for presenting UI screenshots, wiring diagrams, annotated code snippets, and quick sketches that clarify user intent, because agents connected over the Model Context Protocol can read the boards contents. That makes it useful during debugging, design reviews, or when translating visual problems into agent prompts.

How accurate is agent interpretation of board content?

Agent interpretation depends on the downstream model. PromptBoard supplies images and annotations to MCP-compatible agents so they can access visual context, but the quality of any analysis or action depends on the agent's vision and parsing capabilities. The app preserves session state and edit history, which helps maintain consistent context across multiple prompts while teams test how reliably their chosen agent extracts information from screenshots or drawings.

What file types and technical setup does it require?

The server runs in a Node.js environment and requires an MCP-compatible client to exchange context with agents. Typical integrations use clients such as Claude Desktop, Claude Code, or Gemini CLI; connection can be configured by adding the PromptBoard server entry to a client configuration file. Because the server runs locally while agents themselves require internet access, the architecture separates local whiteboard state from remote model calls.

Does it fit naturally into a developer workflow?

The tool targets developers and power users who can install a local Node.js service and edit client configs, and its open-source codebase supports community inspection and contributions. UI options such as dark and light themes and the presence of undo/redo make iterative brainstorming practical. Teams that emphasize reproducible, inspectable agent interactions benefit from keeping the whiteboard on the same machine used for development and testing.

The tool suits developer-stage workflows that need visual context, with validation required

The tool is a practical option for developers and researchers who need a visual context channel for MCP agent workflows. It makes prototyping and exploratory work easier, but teams should validate how their chosen agent interprets visual input before relying on automated outputs in production. Treat the tool as a development and testing asset, pairing it with agent-side checks and human review during integration.