# ALT/FNDATA (pypi · altfinance-mcp)

Auction comparables for luxury & collectibles appraisal: search, stats, price trends, valuations.

- Trust score: 71/100 (medium)
- Change this week: +4
- Registry status: active
- Liveness: live
- Owner verified: no
- Last scored: 2026-11-05

## Components

- remote · `mcp.altfndata.com`: 36/100, [markdown](https://verifymcp.io/servers/com-altfndata-altfinance-mcp/mcp.md), [page](https://verifymcp.io/servers/com-altfndata-altfinance-mcp/mcp)
- pypi · `altfinance-mcp`: 71/100 (this document), [markdown](https://verifymcp.io/servers/com-altfndata-altfinance-mcp/altfinance-mcp.md), [page](https://verifymcp.io/servers/com-altfndata-altfinance-mcp/altfinance-mcp)

## Channel facts

- Registry: `pypi`
- Package: `altfinance-mcp`
- Version: `0.4.7`
- Transport: `stdio`

## Trust breakdown

How this component scores in each security and reliability category. Every signal is checked automatically from public evidence about the published package, including repeated runs of it in an isolated sandbox, and we only credit what we can confirm. Scores are 0–100 per category. Scoring method: https://verifymcp.io/docs/scoring (what has changed: https://verifymcp.io/docs/scoring/changelog)

Scored 2026-11-05.

- **Supply Chain Security**: 100/100
  - No malware found by supply-chain analysis.
  - No known CVEs affecting this package version or its production dependencies.
  - Runs hatchling.build at install time, a recognised build step with no custom scripting around it.
  - 0 of 28 dependencies flagged as unhealthy.
- **Provenance & Transparency**: 45/100
  - Source repository is publicly reachable at the declared URL.
  - Provenance check failed: no build-provenance attestation is published.
  - Clear OSI-approved license (MIT).
  - Actively maintained (last published 9 days ago).
  - Disclosure check failed: no security disclosure policy was found in the source repository.
- **Schema Quality & AI Usability**: 61/100
  - AI-judged instruction clarity (excellent).
  - Context-footprint check failed: tool/resource definitions use about 2448 tokens (~272/item across 9 items; 9 tools + 0 resources), over budget; trim descriptions and params.
  - Usage-examples check failed: none of the tools include examples.
- **Stability & Change Management**: 37/100
  - Stability observed for 11 of 30 days with no destabilising changes; credit accrues until the full window elapses.
- **Tool Coverage**: 71/100
  - 100% of tools have a non-trivial description (not blank, and not just the tool's name).
  - 0% of tool parameters carry a description.
  - Structured output schemas are declared (100% of tools); any adoption earns full credit.
- **Tool Safety**: 100/100
  - No prompt-injection markers were found in the server instructions, tool names or descriptions we captured.
  - We read all 9 captured tool definition(s), and no name or description among them implies an irreversible operation.
  - An AI judge read all 10 captured unit(s) of tool text and found none that tries to manipulate the model reading it.
- **Capabilities**: 100/100
  - Implements a supported MCP spec version (2025-11-25); the latest is 2026-07-28.

## Install

### How do I install the ALT/FNDATA MCP server?

ALT/FNDATA runs locally as a PyPI package, launched with uvx altfinance-mcp. Ready-made configuration for Claude, Cursor, VS Code, Codex and 5 more is on this page, copied from each client's own documentation.

### Claude

```bash
claude mcp add com-altfndata-altfinance-mcp -- uvx altfinance-mcp
```

### Cursor

```json
{
  "mcpServers": {
    "com-altfndata-altfinance-mcp": {
      "command": "uvx",
      "args": [
        "altfinance-mcp"
      ]
    }
  }
}
```

### VS Code

```json
{
  "servers": {
    "com-altfndata-altfinance-mcp": {
      "command": "uvx",
      "args": [
        "altfinance-mcp"
      ]
    }
  }
}
```

### Codex

```bash
codex mcp add com-altfndata-altfinance-mcp -- uvx altfinance-mcp
```

### opencode

```json
{
  "$schema": "https://opencode.ai/config.json",
  "mcp": {
    "com-altfndata-altfinance-mcp": {
      "type": "local",
      "command": [
        "uvx",
        "altfinance-mcp"
      ],
      "enabled": true
    }
  }
}
```

### OpenClaw

```bash
openclaw mcp add com-altfndata-altfinance-mcp --command uvx --arg altfinance-mcp
```

### Hermes

```yaml
mcp_servers:
  com-altfndata-altfinance-mcp:
    command: "uvx"
    args: ["altfinance-mcp"]
```

### Netclaw

```json
{
  "McpServers": {
    "com-altfndata-altfinance-mcp": {
      "Transport": "stdio",
      "Command": "uvx",
      "Arguments": [
        "altfinance-mcp"
      ]
    }
  }
}
```

### Vellum

```bash
assistant mcp add com-altfndata-altfinance-mcp -t stdio -c uvx -a altfinance-mcp
```

### Other

```json
{
  "mcpServers": {
    "com-altfndata-altfinance-mcp": {
      "command": "uvx",
      "args": [
        "altfinance-mcp"
      ]
    }
  }
}
```

## Changelog

Every change recorded for this component, newest first. Days that predate change tracking, or that we cannot explain, say so: "we were watching and nothing happened" and "we were not watching" are different claims.

### 2026-11-05 (score 71, +1)

No change was recorded against any check on this day. Stability & Change Management went from 33 to 37. That category is still filling its 30-day observation window: 10 days of observed history at the previous scan, 11 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-11-03 (score 70, +1)

No change was recorded against any check on this day. Stability & Change Management went from 27 to 30. That category is still filling its 30-day observation window: 8 days of observed history at the previous scan, 9 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-11-01 (score 69, +1)

No change was recorded against any check on this day. Stability & Change Management went from 20 to 23. That category is still filling its 30-day observation window: 6 days of observed history at the previous scan, 7 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-10-30 (score 68, +1)

No change was recorded against any check on this day. Stability & Change Management went from 13 to 17. That category is still filling its 30-day observation window: 4 days of observed history at the previous scan, 5 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-10-27 (score 67, +1)

No change was recorded against any check on this day. Stability & Change Management went from 3 to 7. That category is still filling its 30-day observation window: 1 days of observed history at the previous scan, 2 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-10-26 (score 66, +27)

- [security improvement] Injection markers: unverified → pass
- [security] First check of Judged manipulation: pass
- [functional improvement] Stability: unverified → 0.03
- [functional improvement] Tool coverage: unverified → 100
- [functional improvement] MCP protocol: unverified → pass
- [functional] First check of Schema quality: fail
- [functional] First check of Schema quality: excellent
- [functional] First check of Destructive annotations: pass
- [functional] First check of Schema quality: fail
- [functional] First check of Tool coverage: 100
- [functional] First check of Tool coverage: 0

### 2026-10-25 (score 39)

First indexed and scored.

## MCP tools (9)

### `list_tables` (~167 tokens)

List every auction-data table ALT/FNDATA serves, with live descriptions
and column counts.

Start here whenever a question names a category and you need the right table
alias to pass to get_table_schema, query_table, market_stats or price_trend.
The set spans watches, jewelry (finished/mounted pieces), gems (loose
stones), handbags, cars, motorcycles, wine & whisky, works of art, fine art,
coins, and more. Always call this for the current set rather than assuming
it, and note that loose gemstones live in `gems`, not `jewelry`.

Example: for "what categories of data do you have?" or before "show me the
most expensive coins sold at auction" (to confirm the `coins` alias exists).

Output parameters:

- `result` (string)

### `get_table_schema` (~177 tokens)

List a table's column names and types so you can build a precise query.

Call this before query_table (or before adding `filters` to market_stats /
price_trend) to learn the exact field names a table exposes — e.g. whether
prices are in `usd_price_decimal`, how the brand/designer field is named, or
whether a `carats` column exists. Avoids guessing column names.

Example: before querying Rolex sales, call get_table_schema("watches") to
confirm the brand field is `designer` and the price field is
\`usd_price_decimal`.

Args:
    table: Table alias (call list_tables for the full set). Note: "gems" =
           loose/unmounted stones; "jewelry" = finished/mounted pieces.

Input parameters:

- `table` (string, required)

Output parameters:

- `result` (string)

### `query_table` (~442 tokens)

Query one known table with precise filtering, sorting, and field selection.

Use this when you already know the target table (from list_tables) and want
exact control — e.g. "the 10 most expensive Rolex watches sold since 2020"
becomes table="watches", filters on `designer`/`sale_date`, sort by
\`usd_price_decimal` desc, limit 10. If you don't know which table holds the
item, use `search` (whole-market) instead; for aggregate figures (median,
percentiles) use `market_stats`.

Args:
    table: Table alias (call list_tables for the full set). "gems" = loose
           stones (use for gemstone valuations); "jewelry" = finished pieces.
    fields: List of column names to return (e.g., ["item_title", "vendor", "usd_price_decimal"]).
            If not specified, returns all columns.
    filters: List of filter conditions. Each filter is a dict with:
             - "field": column name
             - "op": operator — one of "eq", "neq", "gt", "gte", "lt", "lte",
               "like", "in", "not_in", "is_null", "is_not_null"
             - "value": the value to compare against
             Example: [{"field": "vendor", "op": "eq", "value": "Phillips"},
                       {"field": "usd_price_decimal", "op": "gte", "value": 50000}]
    sort_field: Column name to sort by (e.g., "usd_price_decimal" or "sale_date")
    sort_direction: "asc" or "desc" (default: "desc")
    limit: Maximum rows to return (default: 20, max: 500)
    offset: Number of rows to skip for pagination (default: 0)

Input parameters:

- `fields`
- `filters`
- `limit` (integer)
- `offset` (integer)
- `sort_direction`
- `sort_field`
- `table` (string, required)

Output parameters:

- `result` (string)

### `market_stats` (~233 tokens)

Summarize the realized-price market for a table or a filtered segment.

Returns count of sales, median, 25th/75th percentiles, average, min/max of
\`usd_price_decimal`, the sale-date span, and the top vendors by volume — all
computed server-side over the full matching set (not a sample).

Args:
    table: Table alias (call list_tables first). "gems" = LOOSE, unmounted
           stones (use for a gemstone-market read); "jewelry" = FINISHED,
           mounted pieces. A loose-stone question can span both.
    filters: Optional list of filter dicts, same shape as query_table
             (field/op/value). Narrow to a segment, e.g.
             [{"field": "designer", "op": "eq", "value": "Patek Philippe"}].
    sold_only: When true (default), restricts to sold lots with a positive
               USD price so the stats reflect realized transactions.

Input parameters:

- `filters`
- `sold_only` (boolean)
- `table` (string, required)

Output parameters:

- `result` (string)

### `price_trend` (~158 tokens)

Show how realized prices moved over time for a table or filtered segment.

Buckets sold lots by sale period and reports, per period, the number of
sales, the median, and the average `usd_price_decimal`.

Args:
    table: Table alias (call list_tables first). Gems = loose stones;
           jewelry = mounted pieces.
    filters: Optional filter dicts (field/op/value), same shape as query_table.
    by: Time bucket — "year" (default), "quarter", or "month".
    sold_only: Restrict to sold lots with a positive USD price (default true).

Input parameters:

- `by` (string)
- `filters`
- `sold_only` (boolean)
- `table` (string, required)

Output parameters:

- `result` (string)

### `price_check` (~228 tokens)

Estimate a single item's fair-value range from comparable auction sales.

Give either a free-text description (e.g. "Hermès Birkin 25 Togo, gold
hardware") OR a lot URL from an auction house. Returns a realized-price range
(low / median / high), a confidence read, and comparable sales with source
links. Best for valuing ONE specific piece; for a whole-market read use
market_stats.

Live comp categories are handbags, watches, and cars. Loose gemstones and
jewelry are intentionally held from comp-matching (under-specified colored
stones give unreliable medians) — for those, use market_stats on `gems`
(loose stones) or `jewelry` (mounted pieces) instead.

Args:
    description: Free-text item description.
    lot_url: A lot URL from an auction house (alternative to description).
    category: One of "handbags", "watches", "cars". Inferred from the text
              if omitted.

Input parameters:

- `category`
- `description`
- `lot_url`

Output parameters:

- `result` (string)

### `search` (~317 tokens)

Whole-market keyword search across every ALT/FNDATA category at once.

Runs a server-side free-text search (the same engine behind the ALT/FNDATA
Search app): your `query` is split into tokens, each matched across
item_title, designer, vendor, gem_type, primary_gem, origin, color, model
and sale_title with synonym expansion (e.g. ruby↔rubis, jade↔jadeite). With
no `category` it fans out across the whole market and merges the top realized
sales by USD price — so you don't have to guess which table holds an item.

Args:
    query: Free-text keywords, e.g. "ruby ring", "Daytona 116500", "Birkin 25".
    category: Optional hint to narrow the search — a table alias
              (e.g. "watches") or a plain word ("cars", "gemstone"). A
              gemstone/stone hint searches BOTH gems (loose) and jewelry
              (mounted); omit it to search everything.
    filters: Optional extra filter dicts (same field/op/value shape as
             query_table), AND-ed with the keyword match — e.g.
             [{"field": "usd_price_decimal", "op": "gte", "value": 50000}].
    limit: Max merged results to return (default 25, max 100).

Input parameters:

- `category`
- `filters`
- `limit` (integer)
- `query` (string, required)

Output parameters:

- `result` (string)

### `market_index` (~240 tokens)

Precomputed model-level price index (headline figures) for a model or brand.

Reads the ALT/FNDATA Index's precomputed hot-stats — instant, no scan. Give a
typed model `query` (e.g. "Hermès Birkin 25", "Rolex Daytona 116500") for a
single model's realized-price band (low / 25th / median / 75th / high), sample
size, and recent trend; or give a `brand` alone for that brand's whole set of
model indices. Covered categories are handbags, watches, and cars. For other
categories, use market_stats (whole-market read) or price_trend instead — this
tool never invents a figure that isn't precomputed.

Args:
    query: A specific model to look up (brand + model + optional size/material).
    brand: A brand to list all model indices for (alternative to query).
    category: One of "handbags", "watches", "cars". Inferred from the text
              when omitted.

Input parameters:

- `brand`
- `category`
- `query`

Output parameters:

- `result` (string)

### `list_alerts` (~169 tokens)

Show ALT/FNDATA price alerts (read-only) or explain how to set them up.

Price alerts watch a saved item and fire when a matching lot sells at or below
your target. They belong to a signed-in ALT/FNDATA account, not to an API key —
so alerts are created and managed in the app (app.altfndata.com), and this MCP
can only read/report them. If your key isn't tied to a signed-in session, this
returns guidance on enabling alerts (allowance: free 0 · registered 1 ·
collector 10 · pro 100 · included with Membership) rather than a data feed.

Args:
    limit: Max fired alerts to show if the alerts feed is reachable (default 25).

Input parameters:

- `limit` (integer)

Output parameters:

- `result` (string)

## Diagnostics

Captured diagnostic sections: Provenance, Install scripts, Dependencies. The full working is on the page: https://verifymcp.io/servers/com-altfndata-altfinance-mcp/altfinance-mcp#diagnostics

## Score history

- 2026-11-05: 71
- 2026-11-04: 70
- 2026-11-03: 70
- 2026-11-02: 69
- 2026-11-01: 69
- 2026-10-31: 68
- 2026-10-30: 68
- 2026-10-29: 67
- 2026-10-28: 67
- 2026-10-27: 67
- 2026-10-26: 66
- 2026-10-25: 39

## Common questions

### What is the ALT/FNDATA MCP server?

ALT/FNDATA is an MCP server listed in the public MCP registry as com.altfndata/altfinance-mcp. Auction comparables for luxury & collectibles appraisal: search, stats, price trends, valuations. This page covers its PyPI package (altfinance-mcp).

### Is the ALT/FNDATA MCP server safe to use?

ALT/FNDATA scores 71 out of 100 on VerifyMCP. We found no known CVEs affecting it as of 5 November 2026. That is a record of what we were able to check automatically, not an endorsement. The category breakdown on this page shows every signal behind the number, including the ones we could not confirm.

### What tools does the ALT/FNDATA MCP server expose?

ALT/FNDATA exposes 9 tools: list_tables, get_table_schema, query_table, market_stats, price_trend, and 4 more. Their descriptions and schemas cost roughly 2,131 tokens of context every time the server is loaded.

### Is the ALT/FNDATA MCP server still maintained?

ALT/FNDATA is still listed as active in the MCP registry. We last reached this channel on 5 November 2026. Those dates come from our own scans of the registry and the channel itself, not from anything the publisher announced.

### What licence is the ALT/FNDATA MCP server under?

ALT/FNDATA declares the MIT licence, which is OSI-approved. That covers the source only, and says nothing about the cost of any service it calls.

## Links

- PyPI project: https://pypi.org/project/altfinance-mcp/
- Socket report: https://socket.dev/pypi/package/altfinance-mcp
- Repository: https://github.com/altfndata/altfndata-mcp
- Website: https://altfndata.com/
- Changelog RSS feed: https://verifymcp.io/servers/com-altfndata-altfinance-mcp/altfinance-mcp.xml
- Changelog JSON feed: https://verifymcp.io/servers/com-altfndata-altfinance-mcp/altfinance-mcp.json
- HTML version of this page: https://verifymcp.io/servers/com-altfndata-altfinance-mcp/altfinance-mcp