Figjam MCP

figjam-context-mcp

A local MCP server that turns FigJam boards into source-cited context for LLMs. It reads a board through the Figma REST API, groups stickies, tables, images and arrows into clusters, and answers questions with links back to the original nodes.

Rendered from the tool calls and outputs of a real run against the public demo board with figjam-context-mcp 0.10.2 and openai/gpt-4.1-mini. Figma's MCP server (use_figma) made the edit.

Why

Workshop boards hold research notes, decisions and open questions, but an LLM cannot read a canvas. Screenshots and PDF exports lose tables, arrow direction and the link to the source. This server keeps the board's structure and cites every answer to the nodes it came from.

Compared with Figma's MCP server

Figma's official MCP server reads a FigJam board or node with its images (get_figjam) and edits boards (use_figma). This server turns a workshop board into context an assistant can cite and track:

• large boards read section by section, with an overview, size-limited pages and search
• source IDs in compact overviews, with Figma links in full excerpts and answer citations
• tables read row by row
• local snapshots, and diff_board to see what changed
• explicit credentials redacted before they reach the cache, the output or a model
• image descriptions from a vision model you configure

The two work together: in the demo above, Figma's server edits the board and this one reports the change.

Tools

ingest_board also reads Figma design files (/design/, /file/ and /proto/ URLs), but clustering and reading order are made for FigJam boards.

Quick start

Five steps. Requires Node.js 22.12 or later. Step 4 sets the model provider that answer_from_board, diagnose_llm_config and image interpretation need; it is optional.

1. Node.js. Check the installed version with node --version; install Node.js 22.12 or later if it is older.
2. Figma token. Create a personal access token under Settings → Security → Personal access tokens with the scope file_content:read. The token's account must be able to open the board. Every ingest_board call is one Figma file download, a Tier 1 request. Figma allows View and Collab seats up to 20 Tier 1 requests a month on every plan, and says the actual limit may be lower (rate limits).
3. Add the server with the example for your client below. The examples set only FIGMA_ACCESS_TOKEN, so the server starts and ingest_board works in ingestMode: "max_speed" without a model provider. @latest with --prefer-online makes npx check the registry for the latest release on every start, even when package metadata is cached (npm configuration).
4. Optional: model provider. Add LLM_BASE_URL and LLM_API_KEY to the server's env block, then restart the client. Both are needed together; without them, balanced ingests run text-only and say that the model settings are missing. Example with OpenRouter (create a key):

   LLM_BASE_URL=https://openrouter.ai/api/v1

LLM_API_KEY=your-openrouter-key

In Claude Code, remove the entry with claude mcp remove figjam-context (with the same --scope as when you added it), then add it again with an --env flag for each variable (Claude Code). A local OpenAI-compatible server without sign-in still needs LLM_API_KEY set to any non-empty value. Set LLM_TEXT_MODELS, LLM_FAST_TEXT_MODELS and LLM_VISION_MODELS to its model names (configuration). Then run diagnose_llm_config in step 5.

1. Check the setup. Ask your assistant to ingest one of your boards with ingestMode: "max_speed", for example *"Ingest https://www.figma.com/board/FILE_KEY/Name with ingestMode max_speed."* That makes one Figma request and no model call. A working setup answers with a line that starts Ingested board FILE_KEY (snapshot …): and counts the clusters. Clients that show their tools, such as Claude Code with /mcp, list the server there (Claude Code). With a provider from step 4, also run diagnose_llm_config; it checks the configured models with small synthetic inputs. For error messages, see common errors.

All settings: configuration.

Claude Code

claude mcp add --scope user --env FIGMA_ACCESS_TOKEN=your-figma-token --transport stdio figjam-context -- npx -y --prefer-online figjam-context-mcp@latest

--scope user makes the server available in all your projects. Without it, Claude Code stores the server for the current project only (scopes).

For longer ingests, set MCP_TOOL_TIMEOUT=300000 in the environment that launches Claude Code and INGEST_BOARD_VISION_BUDGET_MS=270000 in the server environment, leaving about 30 seconds for the rest of the ingest (Claude Code environment variables).

Claude Desktop

Open Settings → Developer → Edit Config in Claude Desktop. This opens claude_desktop_config.json; the file is at:

• macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
• Windows: %APPDATA%\Claude\claude_desktop_config.json

Add the server under mcpServers, keeping any other entries in the file:

{

"mcpServers": { "figjam-context": { "command": "npx", "args": ["-y", "--prefer-online", "figjam-context-mcp@latest"], "env": { "FIGMA_ACCESS_TOKEN": "your-figma-token" } } } }

Then quit Claude Desktop completely and start it again; the server starts with the app (MCP quickstart). The MCP documentation names no tool timeout setting for Claude Desktop, so keep the default image budget.

Codex CLI and ChatGPT desktop app

Add this to ~/.codex/config.toml; the app, CLI and IDE extension share this configuration (OpenAI MCP setup):

[mcp_servers.figjam-context-mcp]

enabled = true command = "npx" args = ["-y", "--prefer-online", "figjam-context-mcp@latest"] startup_timeout_sec = 30 default_tools_approval_mode = "approve"

[mcp_servers.figjam-context-mcp.env] FIGMA_ACCESS_TOKEN = "your-figma-token"

Without default_tools_approval_mode = "approve", ChatGPT may ask for approval or abort tools with external effects; with it, the client never asks before board content goes to the model provider, so use max_speed and avoid answer_from_board for confidential boards.

For longer ingests, set tool_timeout_sec = 300 under [mcp_servers.figjam-context-mcp] and INGEST_BOARD_VISION_BUDGET_MS = "270000" in its env table, leaving about 30 seconds for the rest of the ingest (OpenAI timeout options).

GitHub Copilot CLI

Add this to ~/.copilot/mcp-config.json (Copilot CLI MCP setup):

{

"mcpServers": { "figjam-context": { "type": "local", "command": "npx", "args": ["-y", "--prefer-online", "figjam-context-mcp@latest"], "env": { "FIGMA_ACCESS_TOKEN": "your-figma-token" }, "tools": ["*"] } } }

For longer ingests, set "timeout": 300000 on the server in ~/.copilot/mcp-config.json and "INGEST_BOARD_VISION_BUDGET_MS": "270000" in its env, leaving about 30 seconds for the rest of the ingest (tested with a 600000 ms timeout).

VS Code

In the Copilot harness, add the server under Global → Copilot Global in Chat: Open Customizations (Command Palette), then MCP Servers. VS Code writes it to ~/.copilot/mcp-config.json, the same file as in the Copilot CLI section, in the portable mcpServers format (VS Code MCP servers, configuration reference):

{

"mcpServers": { "figjam-context": { "type": "local", "command": "npx", "args": ["-y", "--prefer-online", "figjam-context-mcp@latest"], "env": { "FIGMA_ACCESS_TOKEN": "your-figma-token" }, "tools": ["*"] } } }

The portable format has no input variables, so the token is stored in this file. Keep that file in your user directory and out of any repository.

Local harness only: to keep the token out of the file, open MCP: Open User Configuration and use an input marked "password": true. VS Code does not forward servers that need inputs to Copilot harness sessions, so this works only in the Local harness (VS Code MCP configuration):

{

"inputs": [ { "type": "promptString", "id": "figma-token", "description": "Figma personal access token (file_content:read)", "password": true } ], "servers": { "figjam-context": { "type": "stdio", "command": "npx", "args": ["-y", "--prefer-online", "figjam-context-mcp@latest"], "env": { "FIGMA_ACCESS_TOKEN": "${input:figma-token}" } } } }

Do not put tokens in a .vscode/mcp.json in a repository; VS Code's documentation says not to commit tokens or passwords.

In the Local harness, "envFile": "${workspaceFolder}/.env" on the server in mcp.json loads an environment file; the portable format does not support envFile, and the server does not load a working-directory .env itself.

For longer ingests, use the Local harness with a per-server "timeout": 300000 in mcp.json and "INGEST_BOARD_VISION_BUDGET_MS": "270000" in its env (about 30 seconds below the timeout); #343135 reports that remote HTTP tool calls in the Copilot harness, the default in 1.142, ignore that timeout and stop