How do AI agents interact with Levelry?

AI agents interact via the Model Context Protocol (MCP) server. They can read canvas state, create/update objects, manage layers, and perform batch operations, translating tool calls into Levelry API requests.

Levelry is a visual knowledge base platform that integrates AI agents with an infinite canvas, allowing ideas, documents, and data to be visually organized and managed through the Model Context Protocol (MCP).

What are the main capabilities of Levelry's MCP for agents?

Agents can read canvas state (list objects, search documents), write to canvas (create, update, connect objects), manage content across multiple projects and specific layers, and perform efficient batch operations.

What are some practical use cases for Levelry with AI agents?

Primary use cases include research & lookup (semantic search), structured capture (adding new concepts), visual mapping (linking dependencies), and AI-assisted design (contextual analysis by an AI copilot).

Levelry

Name: Levelry
Author: kt3v

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Ciencia de Datos y ML

Productividad y Flujo de Trabajo

Aprendizaje y Documentación

Integrate AI agents with a visual knowledge base using the Model Context Protocol.

Levelry provides a visual knowledge base platform featuring an infinite canvas, where ideas are represented as objects with full document capabilities, organized into layers. It incorporates an AI Copilot that understands canvas context and applies domain-specific skills. The Levelry MCP Server acts as a bridge, enabling AI agents to connect to this visual knowledge base via the Model Context Protocol. Agents can read canvas states, write new content, manage layers, and perform batch operations across multiple projects, facilitating advanced AI-assisted design, research, and visual mapping. A pre-configured SKILL.md is included for seamless setup with various MCP-compatible AI tools.

Características Principales

010 GitHub stars

02Connect AI agents to a visual knowledge base via MCP

03Read and write canvas state, including objects and project metadata

04Manage content across multiple projects and specific layers

05Perform batch operations for creating and updating objects efficiently

06Includes ready-made SKILL.md for instant agent setup

Casos de Uso

01Map dependencies and relationships by creating and connecting objects visually

02Conduct research and semantic lookup across object documents

03Capture structured information and visually place new objects on the canvas