Context7
Feeds AI the latest docs for libraries and frameworks before it answers, so code it writes does not use outdated, removed functions.
Description from the maker (original English)
Search, read, create, and organize Workflowy nodes with breadcrumb context.
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Runs on your computer; the tool list appears in your AI app after you install it.
We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 7, 2026
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This one runs on your computer; there is no internet endpoint
Runs on your computer and uses local settings instead
Runs on your computer; we can only read tool lists from internet endpoints
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You need to fill in: WORKFLOWY_API_TOKEN
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The GitHub repo last changed –, and the latest version (1.0.2) reached the registry 11 days ago.
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In the same category: Context7, Codebase Memory, Exa
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Feeds AI the latest docs for libraries and frameworks before it answers, so code it writes does not use outdated, removed functions.
Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.
Connect AI agents to Exa for web search, content fetching, and multi-step research.
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An MCP server that provides full read/write access to Workflowy. Search, create, organize, move, and complete nodes from any MCP-compatible client such as Claude Desktop or Claude Code. The server communicates over stdio and caches Workflowy's export endpoint to stay within API rate limits.
| Variable | Required | Description |
|---|---|---|
WORKFLOWY_API_TOKEN | Yes | Your Workflowy API token |
WORKFLOWY_API_URL | No | Custom API URL (default: https://workflowy.com) |
Requires Go 1.27.3+.
go install github.com/jbeshir/mcp-servers/workflowy/cmd/workflowy-mcp@latest
docker build -t workflowy-mcp ./workflowy
docker run -e WORKFLOWY_API_TOKEN= workflowy-mcp
Add to your Claude Desktop configuration (claude_desktop_config.json):
{
"mcpServers": { "workflowy": { "command": "/path/to/workflowy-mcp", "env": { "WORKFLOWY_API_TOKEN": " " } } } }
claude mcp add workflowy -- env WORKFLOWY_API_TOKEN=<your-token> /path/to/workflowy-mcp
| Tool | Description |
|---|---|
search_nodes | Search nodes by keyword and/or completion date range (completed_after/completed_before, unix seconds) across name and note fields |
get_node | Get full details of a node by ID |
list_children | List child nodes of a parent, sorted by priority |
create_node | Create a new node |
update_node | Update an existing node's properties |
delete_node | Delete a node |
move_node | Move a node to a different parent |
complete_node | Mark a node as completed |
uncomplete_node | Mark a node as not completed |
list_targets | List system locations (home/inbox) and user shortcuts |
home, inbox) and user-defined shortcuts. You can use target keys anywhere a parent ID is accepted, so you can create nodes in your inbox without knowing its UUID.bullets (default), todo, h1, h2, h3, code-block, or quote-block.Projects > Backend > Auth), giving context for where a node sits in the hierarchy.graph LR
Client["MCP Client"] Server["MCP Server 10 tools"] Cache["Export Cache TTL 60s+"] HTTP["HTTP Client Bearer token auth"] API["Workflowy REST API"]
Client -->|"JSON-RPC over stdio"| Server Server -->|"reads"| Cache Server -->|"writes"| HTTP Cache -->|"cache miss"| HTTP HTTP -->|"HTTPS"| API
The server has three internal layers:
internal/server/) — Registers 10 tools, parses arguments, formats JSON responses with breadcrumb paths.internal/cache/) — TTL-based cache of the full node export. Uses double-checked locking (RWMutex) to coalesce concurrent fetches. Minimum TTL is 60 seconds to respect Workflowy's rate limit on the export endpoint.internal/client/) — Thin REST client. All requests use Bearer token authentication. Read operations go through the cache; write operations call the API directly and then invalidate the cache.sequenceDiagram
participant C as MCP Client participant S as MCP Server participant Ca as Export Cache participant H as HTTP Client participant A as Workflowy API
C->>S: search_nodes(query) S->>Ca: GetAllNodes() alt Cache hit (within TTL) Ca-->>S: Cached nodes else Cache miss or expired Ca->>H: ExportNodes() H->>A: GET /api/v1/nodes-export A-->>H: All nodes (JSON) H-->>Ca: []Node Ca-->>S: Fresh nodes end S-->>C: Matching nodes with breadcrumb paths
sequenceDiagram
participant C as MCP Client participant S as MCP Server participant Ca as Export Cache participant H as HTTP Client participant A as Workflowy API
C->>S: create_node(name, parentId) S->>H: CreateNode(request) H->>A: POST /api/v1/nodes A-->>H: Created node ID H-->>S: CreateNodeResponse S->>Ca: Invalidate() S-->>C: "Created node with ID: ..."
Write operations bypass the cache entirely, call the Workflowy API directly, and then invalidate the cache so the next read fetches fresh data.