The backend GET /scan/pending endpoint intentionally returns the whole
inventory — approved devices stay listed so the frontend can show the
canvas-presence badge. The MCP tool proxied that response verbatim while
its description promises devices "not yet approved or hidden", so MCP
clients saw every approved device as if it were still awaiting triage
(59 "pending" entries on a fully-triaged canvas, 58 of them approved).
Filter the tool response to status == "pending", keeping legacy rows
that predate the status field.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01Udrvtwi3cqcxUNHZtBHkBg
Wrap POST /api/v1/designs so AI clients can create a new design (canvas) and get its id back for use as design_id. Completes the create-and-target canvas workflow alongside list_designs.
Co-authored-by: Cursor <cursoragent@cursor.com>
Add an optional design_id argument to create_node, create_edge and get_canvas so AI clients can read and populate a canvas other than the first design. Add a list_designs tool wrapping GET /api/v1/designs so those IDs are discoverable.
Backward compatible: omitting design_id preserves the existing first-design fallback in the backend (nodes.py / edges.py / canvas.py).
Co-authored-by: Cursor <cursoragent@cursor.com>
The MCP create_node/update_node inputSchema only advertised
type/label/ip/hostname/status (plus parent_id on update), so LLM
clients could not set documentation/hardware fields the backend
already validates and stores.
Mirror NodeBase/NodeUpdate by spreading a shared _NODE_FIELDS schema
into both tools: os, notes, mac, check_method, check_target, services,
cpu_count, cpu_model, ram_gb, disk_gb, show_hardware, container_mode,
custom_icon, properties. type stays an enum of canonical node types.
_dispatch already forwarded args verbatim, so no dispatch change.
Also extend _slim_canvas NODE_KEEP so get_canvas round-trips the new
documentation fields the LLM can now write.
Refactor tool definitions into a module-level TOOLS list for direct
schema assertions in tests.
ha-relevant: no
- Add parent_id to NodeUpdate schema in backend so PATCH /nodes/{id}
accepts it (was silently ignored before)
- Expose parent_id in update_node MCP tool schema so the MCP SDK
forwards it to the backend instead of stripping it
- Add regression tests in both backend and MCP layers
- Replace BaseHTTPMiddleware with pure ASGI middleware in auth.py to fix
the "Unexpected message: http.response.start" crash on SSE streams
- Migrate from SseServerTransport to StreamableHTTPSessionManager in main.py
- Add _slim_canvas() in tools.py to strip React Flow layout fields from
get_canvas responses (60-80% payload reduction)
- Update tests: assert canvas slimming, mock session_manager.handle_request
in auth tests to avoid uninitialized task group errors
Exposes homelab topology to MCP-compatible AI clients (Claude Code, etc.)
over LAN via HTTP/SSE on port 8001.
- New mcp/ service: FastAPI + mcp SDK, SSE transport
- Auth: X-API-Key for AI clients, X-MCP-Service-Key for backend (Docker-internal)
- Resources: canvas, nodes, edges, scan/pending, scan/runs
- Tools: create/update/delete nodes+edges, trigger scan, approve/hide devices
- Backend deps.py: accepts JWT or MCP service key (no plain-text password)
- 40 tests (auth, resources, tools) across asyncio + trio
- docker-compose.yml: mcp service on port 8001
- README: MCP setup section with Claude Code/Desktop config examples
Mike Sviblov