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How ALT/FNDATA works in Claude

ALT/FNDATA connects to Claude through the Model Context Protocol, so you can ask about verified auction and private-sale prices in plain language and get answers built from real transaction rows. Here is what happens between your question and the answer.

Open standard (MCP) 3 read-only tools Grounded in transactions Every query visible

What is MCP

The Model Context Protocol (MCP) is an open standard that lets Claude call an external data source live during a conversation. Instead of pasting a spreadsheet into the chat, you connect ALT/FNDATA once and Claude queries it whenever a question needs real numbers. It reads the tool descriptions, decides which to call, fills in the parameters, and folds the result into its answer.

The three-step flow

Every answer follows the same path, whether you are asking for one comparable or a five-year trend.

  1. You ask in plain language

    Name the data ("Using ALT/FNDATA"), a category, and a period. No SQL and no export step. For example: "Using ALT/FNDATA, show the ten highest Patek Philippe watch prices sold in 2024."

  2. Claude queries the connector

    Claude reads the table schema so it uses real column names, then runs one read-only query with the right filters and sort on your behalf.

  3. You get a grounded answer

    The reply is built from verified secondary-market and private-sale rows, not model guesses, and you can see every query Claude ran.

Read-only and auditable

The connector exposes three read-only tools. Nothing is ever written or changed, and every query Claude runs is visible to you, so you can check exactly which table, filters, and sort produced an answer.

Connect the connector

Getting ALT/FNDATA into Claude takes three steps: get an API key from the portal, add the server URL https://mcp.altfndata.com/mcp as a custom connector, and ask your first question. It works in Claude on the web and desktop through the remote connector, or locally for Claude Desktop and Claude Code.

Follow the full Quickstart for step-by-step setup across all three clients, with the exact config and commands.

The three tools

The connector registers three tools. All are read-only and return Markdown-formatted text that Claude reads back to you. Column names are validated live against each table's real schema, so Claude starts with get_table_schema before filtering on a field it is unsure of. You rarely call these yourself; you ask in plain language and Claude picks the tool.

ToolWhat it doesKey parameters
list_tablesLists the tables your key can reach, with a one-line description and column count for each.None
get_table_schemaReturns the column names and types for one table, so filters and sorts use fields that exist.table
query_tableThe workhorse. Filters, sorts, selects fields, and paginates, then returns matching rows.table, fields, filters, sort_field, sort_direction, limit, offset

Filter operators

Each filter is {"field": ..., "op": ..., "value": ...}. Available operators:

OperatorMeaningExample value
eq / neqEquals / not equals"Rolex"
gt / gteGreater than / or equal50000
lt / lteLess than / or equal1000
likeContains (substring match)"Daytona"
in / not_inIn / not in a list["Christie's","Sotheby's"]
is_null / is_not_nullField is empty / present(no value)
Good to know The MCP tools return rows, not server-side aggregates. When you ask for an average or a median, Claude computes it over the rows the query returns (up to 500 per call). For population-level statistics, page through with offset, or use the underlying REST API, which supports aggregations and group_by directly.

What you can reach

Every category shares one wide schema, so the same fields and the same query work across tables. Swap the table alias to move a question from watches to handbags to fine art. The backend registers these aliases; which ones your key can query depends on your plan, so ask Claude to run list_tables to see your set.

AliasCoverage
watchesLuxury watch auction results
handbagsLuxury handbag auction results
jewelryJewelry and gemstone auction results
gemsLoose gemstone auction results
automobileCollector car auction results
motorcycleCollector motorcycle auction results
aircraftAircraft auction results
wine_whiskyWine and whisky auction results
works_of_artWorks of art with stone or jewelry relevance
fine_artFine art across movements and periods

The connector's built-in guidance highlights watches, handbags, and jewelry as the primary set; the other aliases are available where your plan includes them.

Documented fields

These are the fields we document and support across the category tables. Ask Claude to read the full column list for any table with get_table_schema.

FieldWhat it holdsUse it for
designerBrand or maker, for example Rolex or HermesThe main brand filter
modelModel line, for example DaytonaNarrowing within a brand
item_titleFull lot title and descriptionFree-text matching with like
sale_dateDate the lot was offered or soldAny time-based question
usd_price_decimalRealized price in USDPrices; populated when status = sold
sale_estimates_high_usd_pricePre-sale high estimate in USDA forecast, not a paid price
statussold or unsoldFilter to sold before reading a price
vendorAuction house or marketplaceGrouping and source checks
stock_tickerTicker of the brand's public parent, for example CFR.SWLinking a brand to a listed company

Sample prompts

Copy any of these into Claude once the connector is on. Each names the data and a category, and each answer carries the caveat noted below it. The follow-ups show how to drill in without starting over. There is a much fuller set, eight roles and more than twenty recipes, in the use-case playbook.

Collection allocation & liquidity

Family office

Understand what a family's collecting categories are worth at the market level, and how liquid each is, without pretending recent quarters are complete.

Using ALT/FNDATA, for the watches, jewelry, and handbags tables, give me the median sold price and the number of sold lots by year for the last five years. Filter to sold lots only, and note if the most recent year looks incomplete.
Now show the sell-through rate by year for watches: sold lots as a share of all offered lots. Keep unsold lots in the denominator.

Carry: newest quarters undercount; the most recent year is provisional.

Luxury demand read-through to equities

Hedge-fund analyst

Use auction demand as an alternative signal on a listed brand's health, and tie it back to the parent company's ticker.

Using ALT/FNDATA, in the watches table, show quarterly sold-lot counts and median sold price for Rolex and for Patek Philippe over the last three years. Then tell me each brand's parent-company stock ticker from the data.
Rank the top ten brands in the watches table by median sold price over estimate, using usd_price_decimal divided by sale_estimates_high_usd_price, only where the estimate is greater than zero and there are at least twenty sold lots.

Carry: secondary-market demand, not primary sales or revenue; recent quarters provisional.

Client holdings valuation context

Wealth advisor

Give a client a defensible market range for an asset they hold or want, grounded in comparable sold lots rather than a single data point.

Using ALT/FNDATA, find sold Hermes Birkin and Kelly handbags from the last three years. Show the distribution of usd_price_decimal, and the low, median, and high, filtered to sold lots. Note the auction houses represented.
Narrow it to lots whose item_title mentions crocodile or Himalaya, and show how the median shifts.

Carry: a market range from comparables, not an appraisal; excludes retail and dealer asking prices.

Competitive resale benchmarking

Luxury-brand strategist

Benchmark a house's resale strength against rivals: how prices hold versus estimate, and where demand is trending across categories.

Using ALT/FNDATA, compare Cartier and Van Cleef & Arpels in the jewelry table over the last four years. For each, show sold-lot count by year and the median ratio of usd_price_decimal to sale_estimates_high_usd_price, only where the estimate is greater than zero.
Which vendors account for most of each brand's sold lots? Group by vendor and show the top five.

Carry: auction resale behavior, not primary retail performance; recent periods provisional.

Comparable-sales evidence

Appraiser

Assemble a clean, sourced set of comparable sold lots for a valuation file, with the auction house and date on every comparable.

Using ALT/FNDATA, in the watches table, find sold examples of Rolex Daytona reference 116500LN from the last two years. Return item_title, vendor, sale_date, and usd_price_decimal, sorted by sale_date descending, sold lots only.
Page through the next twenty results with offset so I have the full set of comparables.

Carry: comparables from secondary-market sales; verify condition and specifics against each lot.

Market-trend fact-checking

Financial journalist

Stand up or knock down a market-trend claim with sold-lot evidence, and avoid mistaking ingestion lag for a real move.

Using ALT/FNDATA, is the classic-car market cooling? In the automobile table, show sold-lot counts and median sold price by quarter for the last three years. Flag whether the most recent quarters may be incomplete due to ingestion lag.
Break the most recent year down by vendor so I can see which houses drive the totals.

Carry: a dip in the last one to two quarters is likely ingestion lag, not a market decline.

Limits & caveats

The data is honest about what it is. Carry these when you read an answer, and see the coverage and methodology page for the full treatment.

  • Secondary market plus private sales. Coverage is auction results and private-sale transactions, not retail list prices or dealer asking prices.
  • The newest one to two quarters undercount. Recent sales are still being ingested, so the latest periods look thinner than they are. Treat very recent volume and counts as provisional, not as a market decline.
  • Read prices only on sold lots. usd_price_decimal is populated when status = sold. Estimates are forecasts, not amounts paid.
  • Prices are USD-normalized using nearest-date exchange rates, so a converted figure can differ slightly from a same-day conversion.
  • Rows, not aggregates, over the wire. Any average or median Claude reports is computed over the returned rows, up to 500 per call. For population-level statistics, page through results or use the REST API.
  • Not investment advice. The data is for research and reference.
Questions Reach us at info@altfndata.com.

Where to next

  • Quickstart. Get a key and connect the connector in Claude web, Desktop, or Claude Code.
  • Use-case playbook. Eight roles and more than twenty copy-paste recipes, each with its caveat.
  • Technical docs. The three tools in full, the dataset and field reference, auth, and limits.
  • Coverage and methodology. What the data covers, how fresh it is, and how to trust an answer.
Questions Reach us at info@altfndata.com.

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