watchcharts-mcp
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@watchcharts-mcpWhat is the current market price of a Rolex Daytona 116503?"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
watchcharts-mcp
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 |
| Count of watches matching filters |
| Paginated screener results (DataTables format, HTML fragments parsed into JSON) |
| Model/brand autocomplete (pure JSON) |
| Model page: full specs, market/retail price (parsed) |
| Price series, auction sales, historical listings (pure JSON) |
| Overall / per-brand market index since 2017 (pure JSON) |
| Full catalog browse with spec filters (server-rendered, parsed) |
| Live eBay listings aggregation |
| Marketplace listing search (server-rendered, parsed) |
| 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.
Related MCP server: mcp-financex
Requirements
macOS/Linux/Windows with Google Chrome installed
Python ≥ 3.12, uv
Tools
Tool | Description |
| Screener results: name, collection, watch_id, url, image, market price (EUR), trend % |
| Count matching watches |
| All brands with WatchCharts brand ids |
| Resolve free text ("daytona 116503") to watch_id, uuid, brand, collection, price |
| Full specs (references, complications, case, dial), market + retail price, per-variation prices |
| Auction sale records (Sotheby's, Christie's...) with hammer price |
| Historical sold/unsold listings across eBay, dealers, forums |
| Daily market price series (~1y on free tier) + retail price |
| Overall or per-brand market index, daily since 2017 |
| Filter the 29k+ catalog by specs (dial, diameter, movement, complications...) |
| Live eBay listings: title, seller, price, URL |
| Marketplace listings (dealers, Reddit, forums): price, Fair/Good/High rating, country |
| Listing detail: price, value assessment vs estimate, source, external URL |
| 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 syncClaude Code
claude mcp add watchcharts -- uv run --directory /path/to/watchcharts-mcp watchcharts-mcpClaude 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/fixturesParsers 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.
Maintenance
Resources
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