huckleberry-mcp-worker
An always-on MCP server that lets AI assistants read and write sleep, feeding, diaper, and growth records for the Huckleberry baby-tracking app through a Cloudflare Worker using the Firebase REST API.
README
huckleberry-mcp-worker
A Model Context Protocol server for the Huckleberry baby-tracking app, running as a Cloudflare Worker.
This is a TypeScript port of
bckenstler/py-huckleberry-mcp.
The original is a Python stdio server that talks to Firestore through
google-cloud-firestore, which speaks gRPC and therefore cannot run on Workers.
This port reaches the same backend over the Firebase REST API using fetch, and
serves MCP over Streamable HTTP.
The practical difference: it is always on. No laptop has to be awake for a client to log a nap.
Tools
All 23 tools from the Python server are implemented, plus delete_record.
| Area | Tools |
|---|---|
| Children | list_children, get_child_name |
| Sleep | log_sleep, start_sleep, pause_sleep, resume_sleep, complete_sleep, cancel_sleep, get_sleep_history |
| Feeding | log_breastfeeding, log_bottle_feeding, start_breastfeeding, pause_feeding, resume_feeding, switch_feeding_side, complete_feeding, cancel_feeding, get_feeding_history |
| Diaper | log_diaper, get_diaper_history |
| Growth | log_growth, get_latest_growth, get_growth_history |
| Records | delete_record |
Every history tool reports each record's interval_id, which is what
delete_record takes.
Fixes relative to the Python server
Four defects were found while porting and are corrected here.
Breastfeeding durations were written in the wrong unit. The backend stores
leftDuration / rightDuration in seconds — that is what the app's own timer
writes — but log_breastfeeding passed the caller's minutes straight through.
Logging a 5 minute feed recorded 5 seconds. Callers previously had to pass 300
to mean 5 minutes; here left_duration_minutes: 5 means five minutes.
Single-day history queries returned nothing. Both ends of a date range were
resolved to midnight, so start_date == end_date produced an empty window and
the server reported no records for a day that had plenty. Ranges are now
half-open [start_of_start_date, start_of_end_date + 1 day), making both ends
inclusive.
end_time in sleep history was always null. The code read an end field
that the backend never writes. It is now derived from start + duration.
birth_date in list_children was always null. The backend field is
birthdate; the server read birthDate.
get_feeding_history also now returns each record's mode, plus the details
that go with it: amount and type for bottles, food names and reactions for
solids. Without them a solids record is an empty row, indistinguishable from a
zero-length nursing session — which is exactly how a perfectly good record gets
mistaken for a missing one.
Setup
Requires Node 18+ and a Cloudflare account.
npm install
npx wrangler login
Set the secrets — they are stored encrypted by Cloudflare and never live in the repository:
npx wrangler secret put HUCKLEBERRY_EMAIL
npx wrangler secret put HUCKLEBERRY_PASSWORD
npx wrangler secret put MCP_AUTH_TOKEN # a long random string you generate
npx wrangler secret put HUCKLEBERRY_TIMEZONE # e.g. America/Sao_Paulo
HUCKLEBERRY_TIMEZONE defaults to America/New_York. It decides how naive
datetimes like "2026-08-17T15:47:00" are interpreted, so setting it correctly
matters.
Deploy:
npm run deploy
Authentication
The Worker URL is public, and the server holds credentials to a child's health record, so every request must carry the bearer token:
Authorization: Bearer <MCP_AUTH_TOKEN>
Requests without a valid token get a 401 before any Huckleberry call is made.
Generate a token with something like openssl rand -base64 32.
Clients that cannot send headers
Some MCP clients accept only a URL — claude.ai custom connectors, for one, take
a URL and optional OAuth credentials with no field for Authorization. For
those, the server also accepts the token as the last path segment:
POST https://<your-worker>.workers.dev/mcp/<MCP_URL_TOKEN>
MCP_URL_TOKEN is a separate secret from MCP_AUTH_TOKEN, and deliberately
so: request paths end up in access logs, browser history, and referrers in a way
headers do not. Keeping them apart means a leak through a URL does not
compromise the header credential, and either can be rotated on its own. If
MCP_URL_TOKEN is unset the route falls back to MCP_AUTH_TOKEN, which is
convenient but gives up that separation.
npx wrangler secret put MCP_URL_TOKEN
Prefer the header route wherever the client supports it.
Client configuration
For Claude Code:
claude mcp add --transport http huckleberry https://<your-worker>.workers.dev/mcp \
--header "Authorization: Bearer <MCP_AUTH_TOKEN>"
Local development
cp .dev.vars.example .dev.vars # then fill it in; .dev.vars is gitignored
npm run dev
curl -X POST http://localhost:8787/mcp \
-H "Authorization: Bearer $TOKEN" \
-H 'Content-Type: application/json' \
-H 'Accept: application/json, text/event-stream' \
-d '{"jsonrpc":"2.0","id":1,"method":"tools/list","params":{}}'
Design notes
Stateless. Each request builds a fresh McpServer via createMcpHandler
from Cloudflare's agents SDK. No Durable Objects and no session storage are
involved, because every tool is a self-contained read or write.
Token caching. Firebase ID tokens last an hour and are cached in module scope, so requests landing on a warm isolate skip re-authentication. A cold isolate costs one extra round trip. A token rejected mid-flight triggers one re-authentication and retry.
Numeric types. Firestore distinguishes integers from doubles, and the app
writes some fields as one and some as the other. Values that must be stored as
doubles are wrapped in dbl() so records written here match records written by
the app.
Multi-entry documents. History lives in two shapes: ordinary documents with
a top-level start, and batch documents holding many entries under data.
Nested starts cannot be filtered server-side, so batch documents are fetched
whole and filtered in the Worker. Records report which shape they came from via
is_multi_entry.
Deleting records
The Python server had no delete, and the received wisdom was that the backend
did not allow one. It does: a DELETE on the document path returns 200. What
was actually missing was the record's id, which the history tools never
reported.
So history tools now return interval_id, and delete_record removes the
record it names. Batched entries — several records packed into one document
under data — are addressed as <documentId>#<entryKey> and removed as a
field of their parent.
Deleting also repoints prefs.last* at the newest surviving record. The app
reads those pointers directly, so a delete without the repoint leaves it showing
a record that no longer exists.
Known limitations
- Deletes are permanent. There is no undo. Confirm with a history query
before calling
delete_record. - Solids are read-only.
get_feeding_historyreports solids entries with their food names and reactions, but there is no tool to create one. start_sleepdoes not guard against an already-running timer. The Python server documented that it would fail in that case but never checked; the behaviour is preserved here rather than silently changed.- Notes do not round-trip into sleep records. The
detailsfield is a fixed structure of checkboxes, not free text.
License
MIT
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