Watchcharts
FreeNot checkedMCP server for WatchCharts.com that provides luxury watch market data including prices, trends, sales, listings, and appraisals through various tools.
About
MCP server for WatchCharts.com that provides luxury watch market data including prices, trends, sales, listings, and appraisals through various tools.
README
MCP server for watchcharts.com — luxury watch market prices and trends, exposed as tools for Claude and other MCP clients.
How it works
WatchCharts is protected by a Cloudflare JS challenge that blocks plain HTTP clients and vanilla headless browsers. This server uses patchright (stealth-patched Playwright) driving your system Google Chrome in headful mode, with the window parked off-screen. API calls run inside the page via fetch(), reusing the browser's cookies, TLS fingerprint and CSRF token.
The private REST API was reverse-engineered from HAR captures of the /market screener and the marketplace:
| Endpoint | Purpose |
|---|---|
GET watchcharts.com/rest/screener_count |
Count of watches matching filters |
GET watchcharts.com/rest/market_results |
Paginated screener results (DataTables format, HTML fragments parsed into JSON) |
GET watchcharts.com/suggest/analytics/0.json?q=… |
Model/brand autocomplete (pure JSON) |
GET watchcharts.com/watch_model/{id}/overview |
Model page: full specs, market/retail price (parsed) |
GET watchcharts.com/charts/watch/{id}.json?type=trend|sales|listings |
Price series, auction sales, historical listings (pure JSON) |
GET watchcharts.com/charts/brand.json / charts/brand/{id}.json |
Overall / per-brand market index since 2017 (pure JSON) |
GET watchcharts.com/watches?filters=<b64>&page=&sort= |
Full catalog browse with spec filters (server-rendered, parsed) |
GET marketplace.watchcharts.com/rest/ebay |
Live eBay listings aggregation |
GET marketplace.watchcharts.com/listings?q=… |
Marketplace listing search (server-rendered, parsed) |
GET marketplace.watchcharts.com/listing/{id}?html=true |
Listing detail with value assessment |
filters is base64-encoded JSON: key -1 = price cap, keys -100…-105 = time period (1m…5y) with minimum trend %. Brand filtering uses the brandId query param. Each subdomain has its own CSRF token (read from #csrfToken[data-token]), but the Chrome context/cookies are shared.
Requirements
- macOS/Linux/Windows with Google Chrome installed
- Python ≥ 3.12, uv
Tools
| Tool | Description |
|---|---|
search_watches(price_max, period, min_trend_pct, brand_id, start, limit) |
Screener results: name, collection, watch_id, url, image, market price (EUR), trend % |
count_watches(query, brand_id, price_max, period, min_trend_pct) |
Count matching watches |
list_brands() |
All brands with WatchCharts brand ids |
search_models(query) |
Resolve free text ("daytona 116503") to watch_id, uuid, brand, collection, price |
get_watch_info(watch_id) |
Full specs (references, complications, case, dial), market + retail price, per-variation prices |
get_watch_sales(watch_id) |
Auction sale records (Sotheby's, Christie's...) with hammer price |
get_listings_history(watch_id) |
Historical sold/unsold listings across eBay, dealers, forums |
get_price_history(watch_id) |
Daily market price series (~1y on free tier) + retail price |
get_market_index(brand_id) |
Overall or per-brand market index, daily since 2017 |
browse_watches(filters, page, sort) |
Filter the 29k+ catalog by specs (dial, diameter, movement, complications...) |
search_ebay(query, fallback_query, watch_id, country) |
Live eBay listings: title, seller, price, URL |
search_listings(query, page) |
Marketplace listings (dealers, Reddit, forums): price, Fair/Good/High rating, country |
get_listing(listing_id) |
Listing detail: price, value assessment vs estimate, source, external URL |
appraise_watch(query, condition, accessory, region) |
Instant appraisal: estimated value adjusted for condition, box/papers, region |
watch_id from search_watches composes with get_price_history and search_ebay. variation_id from get_watch_info narrows get_price_history, get_watch_sales, and get_listings_history to a single reference/dial.
Appraisal (captcha-gated, driven via the real form)
appraise_watch is the equivalent of the paid API's appraisal.
Usage:
appraise_watch(
query="Rolex Daytona 116503", # name or reference
condition="Pre-owned", # or "New" / "Unworn"
accessory="box and papers", # or "box only" / "watch only"
region="Europe", # or "North America" / "Asia"
)
# → {"watch": "Rolex Cosmograph Daytona 116503",
# "estimated_value": "€17,671",
# "condition": "Pre-owned", "accessory": "Watch with original box and papers",
# "region": "Europe", "summary": "..."}
condition / accessory / region are matched by substring against the form's dropdown labels, so partial words work. The response echoes back the label actually matched. The inputs move the number — e.g. new + watch-only + North America → €18,058.
Why it's different from the other tools. Its submit endpoint is protected by a per-request captcha token that only the page's own JS can mint. There is no token stored anywhere in this code — each call drives the real /appraisal form (type reference → pick the match → set the dropdowns → submit), and the browser mints a fresh token at submit time. That's why "will it still work tomorrow?" is a yes: nothing is cached that can expire. The only prerequisites are the same as every other tool — Chrome installed and Cloudflare passing.
What can break it. Because it's UI-driven, it depends on the form's DOM structure. If WatchCharts redesigns the /appraisal form, update APPRAISAL_SELECTORS in client.py — that dict is the single place all the selectors live. The parsing of the rendered result is separate (_parse_appraisal_report) and covered by an offline fixture test, so a wording change in the report surfaces as a test failure. It's ~10-15s per call (a real browser flow) and more fragile than the JSON-backed tools — use it for one-off valuations, not bulk lookups.
Install
git clone https://github.com/NiccoloSalvini/watchcharts-mcp
cd watchcharts-mcp
uv sync
Claude Code
claude mcp add watchcharts -- uv run --directory /path/to/watchcharts-mcp watchcharts-mcp
Claude Desktop
{
"mcpServers": {
"watchcharts": {
"command": "uv",
"args": ["run", "--directory", "/path/to/watchcharts-mcp", "watchcharts-mcp"]
}
}
}
Development
uv run pytest # offline parser tests against fixtures in tests/fixtures
Parsers are pinned by fixture tests: if WatchCharts redesigns its markup, tests fail instead of tools returning silently empty data.
Notes
- First tool call launches Chrome and solves the Cloudflare challenge (~15–30 s); later calls are fast. The Chrome profile is cached in
~/.cache/watchcharts-mcp/chrome-profileso subsequent launches reusecf_clearance. - Free-tier data: results are capped by WatchCharts (2000 rows) and some columns require a Professional subscription.
- For personal/research use. Respect WatchCharts' terms of service.
Install Watchcharts in Claude Desktop, Claude Code & Cursor
unyly install watchcharts-mcpInstalls into Claude Desktop, Claude Code, Cursor & VS Code — handles npx, uvx and build-from-source repos for you.
First time? Get the CLI: curl -fsSL https://unyly.org/install | sh
Or configure manually
Run in your terminal:
claude mcp add watchcharts-mcp -- uvx --from git+https://github.com/NiccoloSalvini/watchcharts-mcp watchcharts-mcpFAQ
Is Watchcharts MCP free?
Yes, Watchcharts MCP is free — one-click install via Unyly at no cost.
Does Watchcharts need an API key?
No, Watchcharts runs without API keys or environment variables.
Is Watchcharts hosted or self-hosted?
Self-hosted: the server runs locally on your machine via the install command above.
How do I install Watchcharts in Claude Desktop, Claude Code or Cursor?
Open Watchcharts on unyly.org, pick your client tab (Claude Desktop, Claude Code, Cursor) and press Install — the config is generated automatically, no JSON editing.
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