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JSON Canvas MCP Server

@Cam10001110101

Repository

Basic information

Category

Memory & Knowledge

License

MIT

Transports

stdio

Author

Cam10001110101

About JSON Canvas MCP Server

No overview available yet

#jsoncanvas#mcp-server#canvas-tools

JSON Canvas MCP Server config

Add this server to your MCP-compatible client using the configuration below.

{
  "mcpServers": {
    "mcp-server-obsidian-jsoncanvas": {
      "command": "docker",
      "args": [
        "build",
        "-t",
        "mcp/jsoncanvas",
        "."
      ]
    }
  }
}

JSON Canvas MCP Server tools

7

Create a canvas from `nodes` (and optional `edges`) and write it to a

Validate canvas data against the JSON Canvas 1.0 specification.

Read a stored `.canvas` file and return its nodes and edges.

List the `.canvas` files available in `OUTPUT_PATH`.

Add, update, and/or remove nodes and edges on a stored canvas in one

Export a stored canvas to another format.

Case-insensitive substring search across stored canvases.

Overview

What is JSON Canvas MCP Server?

An MCP (Model Context Protocol) server for working with JSON Canvas files (.canvas), the open infinite-canvas format used by Obsidian. It allows an MCP client to create, validate, read, list, edit, export, and search canvas files. Built on the mcp Python SDK (>=1.27) and runs over stdio or Streamable HTTP transport.

How to use JSON Canvas MCP Server?

Install via Docker or uv. Configure the server with environment variables (e.g., OUTPUT_PATH). Invoke with mcp-server-jsoncanvas. Integrate with Claude Desktop by adding a stanza to claude_desktop_config.json (see examples for Docker and uv). The server can also be used as a Python library via the jsoncanvas package.

Key features of JSON Canvas MCP Server

  • Five tools: create_canvas, validate_canvas, read_canvas, list_canvases, edit_canvas, export_canvas, search_canvases
  • Interactive canvas viewer (MCP Apps UI) for UI-capable hosts
  • Export canvases to Markdown (edge-ordered outline) or SVG (vector image)
  • Atomic edit operations (add/update/remove nodes and edges)
  • Case-insensitive substring search across stored canvases

Use cases of JSON Canvas MCP Server

  • Creating and saving structured infinite canvas files from AI conversations
  • Validating canvas data against the JSON Canvas 1.0 specification
  • Reading and editing existing canvases programmatically
  • Searching across multiple canvases for specific content
  • Exporting canvases as Markdown outlines or SVG images

Frequently asked questions

1

What transports does the server support?

It supports stdio (default) and Streamable HTTP. Set `MCP_TRANSPORT` environment variable or use the `--transport streamable-http` command-line flag.

2

How do I configure the server?

Set the `OUTPUT_PATH` environment variable to the directory where `.canvas` files are stored (default `./output`). For HTTP transport, optionally set `MCP_HOST`, `MCP_PORT`, and `MCP_CORS_ORIGINS`.

3

Can I use the server as a Python library?

Yes. The `jsoncanvas` package can be imported directly to create `Canvas` objects, add nodes/edges, and serialize to JSON.

4

Is the HTTP transport secure?

No. It is unauthenticated and intended for local use only. The server binds to `127.0.0.1` by default and rejects non-localhost `Host`/`Origin` headers. For remote access, front it with an authenticating reverse proxy.

5

Does the interactive canvas viewer require a UI?

Only MCP hosts that support the MCP Apps UI extension can render the interactive canvas viewer. Text-only clients receive canvas data as structured output.

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