rijksmuseum-mcp

分类: 工具与效率 | 上传者: kintopp | 下载: 0 | 版本: v1.0（最新）
Rijksmuseum MCP+ 服务器的研究工作流程。每当用户想要搜索国家博物馆收藏、查找艺术品详细信息、探索图像主题、检查高分辨率图像、追踪出处、浏览精选集或回答任何可以通过国家博物馆收藏的约 832,000 件艺术品来解决的艺术历史问题时，都可以使用此技能。
更新日志: Source: GitHub https://github.com/kintopp/rijksmuseum-mcp-plus
目录结构

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📁 references/
- 📄 provenance-patterns.md 13.6 KB
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SKILL.md

---
name: rijksmuseum-mcp
description: >
  Research workflows for the Rijksmuseum MCP+ server. Use this skill whenever
  the user wants to search the Rijksmuseum collection, look up artwork details,
  explore iconographic themes, inspect high-resolution images, trace provenance,
  browse curated sets, or answer any art-historical question that could be
  addressed through the Rijksmuseum's holdings of ~832,000 artworks.
metadata:
  version: "0.23.5"
  last_updated: "2026-04-04"
---

# Rijksmuseum MCP+ Research Skill

## Core Mental Model

The server is not a 'one and done' search engine — it is a resource for actively exploring and researching the collection of the Rijksmuseum. Many meaningful questions will require several chained tool calls. The canonical sequence is:
```
DISCOVER → QUANTIFY → RETRIEVE → INSPECT
```
Avoid jumping directly to RETRIEVE without a DISCOVER or QUANTIFY step unless the query is simple and straightforward (e.g. the object number is already known). For QUANTIFY, use `collection_stats` for distributions and cross-domain breakdowns, `compact: true` for simple counts.

---

## Tool Selection Guide

| Question type | Start here |
|---|---|
| "Find works about X" — clear vocabulary concept | `search_artwork` with `subject` |
| "Find works about X" — interpretive / atmospheric | `semantic_search` |
| "Find works depicting [iconographic scene]" | Iconclass server `search` → `search_artwork` with `iconclass` |
| "How many works match X?" | `collection_stats` for counts/distributions; `search_artwork` with `compact: true` only when you need object numbers |
| "Distribution of X across the collection" | `collection_stats` with `dimension` |
| "Top N creators / depicted persons / places" | `collection_stats` with `dimension` + `topN` |
| "Sales by decade" / time series | `collection_stats` with `dimension: "provenanceDecade"` |
| "How many artworks have LLM-mediated interpretations?" | `collection_stats` with `dimension: "categoryMethod"` |
| "Of artworks with provenance, how many are paintings?" | `collection_stats` with `dimension: "type"` + `hasProvenance: true` |
| "What does the Rijksmuseum say about this work?" | `get_artwork_details` |
| "Examine this image closely / read this inscription" | `inspect_artwork_image` |
| "Show me a curated group of works on X" | `list_curated_sets` → `browse_set` |
| "Has anything changed / been acquired recently?" | `get_recent_changes` |
| "Who owned this work / trace its ownership chain" | `search_provenance` with `objectNumber` |
| "Which works passed through collector X?" | `search_provenance` with `party` |
| "Find confiscations / sales / transfers in city Y" | `search_provenance` with `transferType`, `location` |
| "How long did family X hold their collection?" | `search_provenance` with `layer: "periods"`, `ownerName` |

**Choosing between `search_artwork(provenance=...)` and `search_provenance`:**
`search_artwork` with `provenance` is a keyword search over raw provenance text — fast for counting, faceting, and cross-tabulation (e.g. "how many Mannheimer works are porcelain?"). `search_provenance` returns structured, parsed chains with dates, prices, transfer types, and ownership periods — use it when you need to reason about the *sequence* of ownership, filter by event type, or rank by price or duration. For the last link in the chain — how the Rijksmuseum acquired a work — also check `creditLine`, which covers ~358K artworks (vs ~48K with parsed provenance) and often names donors or funds absent from the provenance chain (e.g. "Drucker-Fraser", "Vereniging Rembrandt").

---

## Critical Parameter Distinctions

### `productionPlace` vs `depictedPlace`

These are semantically opposite and getting them wrong produces systematically
wrong results.

- `productionPlace`: where the object was **made** — "Amsterdam", "Delft"
- `depictedPlace`: what place the object **shows** — "Batavia", "Nagasaki"

A Dutch painting *of* Batavia was made in Amsterdam. Use both together to
distinguish objects *from* Asia versus European images *of* Asia.

### `subject` vs `iconclass`

- `subject`: morphological stemming against ~108K terms (almost all in English); best first pass; try natural phrases ("winter landscape", "vanitas", "civic guard")
- `iconclass`: the Iconclass classification system — ~40,675 notations across 13 languages. A retrieval tool, not a descriptive language: a notation's meaning comes from its position in the hierarchy, not just its label. Use the **Iconclass server** (`search`, `browse`, `resolve`, `expand_keys`, `search_prefix`) to discover notation codes by keyword, concept, or hierarchy navigation — see its SKILL file for full workflows and query patterns. Each result includes collection counts signalling how many artworks carry that notation. Pass the code to `search_artwork`'s `iconclass` parameter for precise filtering.

**Decision rule:** start with `subject` — it's faster and handles most queries well. Switch to `iconclass` when:
- you need to distinguish closely related scenes (Crucifixion vs Deposition, Annunciation vs Visitation) — these are sibling notations with distinct codes
- `subject` returns too broad a result set and you need hierarchical precision
- you want to explore a conceptual neighbourhood (browse children/siblings of a notation)
- the query is in a non-English language (Iconclass covers 13 languages; subject vocab is almost entirely English)
- you want to search by meaning rather than keywords (semantic mode: "religious suffering", "festive gathering")
- you need to combine multiple iconographic concepts in a single query (AND-combined codes)

### `dateMatch` — controlling how date ranges are binned

Most artworks have date *ranges* (e.g. "1630–1640"), not exact years. `dateMatch` controls how these ranges interact with `creationDate` wildcards and `collection_stats` decade/century dimensions:

| Mode | Behaviour | Use when |
|---|---|---|
| `"overlaps"` (default) | Artwork appears in every bin its range touches | Inclusive discovery — "show me everything that *could* be from the 1630s" |
| `"within"` | Artwork appears only if its entire range falls within the bin | Exclusive bins, but drops ~43% of the collection (broadly-dated objects) |
| `"midpoint"` | Each artwork counted in exactly one bin (midpoint of its date range) | **Statistical comparisons, charts, decade-by-decade counts** — no double-counting, no data loss |

**Rule of thumb:** use `midpoint` whenever you're comparing counts across bins (decade trends, century breakdowns, gender ratios by period). Use the default `overlaps` for discovery queries where inclusiveness matters more than precision.

Available on `search_artwork`, `semantic_search`, and implicitly via `collection_stats` `creationDateFrom`/`creationDateTo` filters.

### `attributionQualifier` + `creator` — structural limitation

The parameter description suggests combining `attributionQualifier` with `creator` to filter by source artist (e.g. "find all followers of Rembrandt"). **This combination never works — the tool schema documentation is incorrect.** Verified across all qualifier types (`follower of`, `workshop of`, `circle of`, `attributed to`): the structured `creator` field is always `Unknown [painter]` or `anonymous`, regardless of qualifier. The source artist's name appears only in the composite display string and is not exposed as a searchable entity field.

**The correct strategy by qualifier type:**

| Goal | Working approach |
|---|---|
| Works in the manner/style of artist X | `aboutActor: "X"` + `type: "painting"` (or other type) |
| All "follower of" works in the collection | `attributionQualifier: "follower of"` alone (returns ~111 works) |
| Sub-filter those by source artist | Not possible via parameters — requires fetching and inspecting individual records |

**Additional complication — "manner of" is not a valid qualifier value.** The Rijksmuseum uses "manner of" as a distinct attribution category, but `"manner of"` is not in the `attributionQualifier` controlled vocabulary and returns zero results. Works attributed in this way are retrievable only via `aboutActor`.

**Canonical name form matters.** The Rijksmuseum catalogue uses historical Dutch/Latin spellings for some artists. Bosch is catalogued as **"Jheronimus Bosch"**, not "Hieronymus Bosch". Always check `get_artwork_details` on a known work to confirm the canonical form before filtering.

### `creator` vs `aboutActor` vs `depictedPerson`

- `creator`: who made it — uses canonical name forms ("Rembrandt van Rijn")
- `depictedPerson`: who is *shown* in it — matches 210K name variants including historical forms
- `aboutActor`: broader cross-field search; more tolerant of name variants and cross-language forms ("Louis XIV" finds "Lodewijk XIV")

### `search_provenance`: two query layers

`search_provenance` has two data layers that answer fundamentally different questions.

- `layer: "events"` (default): individual transactions — each with a date, location, price, parties (with roles and positions), and transfer type. Think of it as a ledger of *what happened*. Events-only parameters: `transferType`, `excludeTransferType`, `hasPrice`, `currency`, `hasGap`, `relatedTo`.
- `layer: "periods"`: interpreted ownership spans — who held the work, how they acquired it, and for how long. Think of it as a timeline of *who owned what*. Periods-only parameters: `ownerName`, `acquisitionMethod`, `minDuration`, `maxDuration`, `sortBy: "duration"`.

Shared parameters work on both layers: `party`, `location`, `creator`, `dateFrom`/`dateTo`, `objectNumber`, `categoryMethod`, `positionMethod`, `sortBy`, `offset`, `facets`.

**`dateFrom`/`dateTo` semantics differ by layer:**
- **Events**: filters on the event's `date_year` — "something happened between these years"
- **Periods**: `dateFrom` filters on `begin_year`, `dateTo` on `end_year` — "ownership that started after X AND ended before Y"

The periods interpretation is much more restrictive — `dateFrom=1933, dateTo=1945` on periods misses any ownership that started before 1933 or extended past 1945. **For date-range queries (especially wartime provenance), prefer the events layer.**

**Anti-join pattern** (`transferType` + `excludeTransferType`): artwork-level set difference. `transferType: "confiscation", excludeTransferType: "restitution"` returns artworks that were confiscated but *never* restituted. Note: items *recuperated* (recovered by Allied forces) are not the same as items *restituted* (formally returned to original owners) — they will appear in anti-join results.

**Filter requirement**: both layers reject bare queries. At least one content filter is required. If you need a collection-wide ranking, use a broad filter such as `dateFrom: 1400` as a catch-all.

For the full provenance data model — AAM text format, transfer type vocabulary, party roles and positions, date/currency representations, and tested query patterns — see `references/provenance-patterns.md`.

### Full-text filters vs vocabulary filters (ranking matters)

Results rank by **BM25 text relevance** when any of these are active:
`title`, `description`, `inscription`, `provenance`, `creditLine`,
`curatorialNarrative`.

Results rank by **importance** (image availability + curatorial attention +
metadata richness) when only vocabulary or column filters are active.

**Practical rule**: if you want the most *significant* works to surface first,
use vocabulary filters alone. If you want *keyword relevance*, activate a text
filter.

---

## Modifier Parameters (cannot stand alone)

These narrow results but **require at least one other content filter**:

| Modifier | Notes |
|---|---|
| `imageAvailable: true` | Always pair with a content filter |
| `hasProvenance: true` | Restricts to ~48K artworks with parsed provenance. Pair with `type`, `creator`, etc. for cross-domain queries. |
| `creatorGender: "female"` | Pair with `type`, `subject`, or `creationDate` |
| `creatorBornAfter` / `creatorBornBefore` | Pair with any content filter |
| `expandPlaceHierarchy: true` | Expands place filters 3 levels deep; pair with `productionPlace` etc. |

---

## Key Workflows

### 1. Scope Before You Browse

For any counting, distributional, or comparative question, **start with `collection_stats`** — it answers in one call what would otherwise require multiple `compact: true` loops.

```
# Example: Rembrandt's output across media ��� one call
collection_stats(dimension="type", creator="Rembrandt van Rijn")
# → painting 314 (38.2%), print 289 (35.2%), drawing 218 (26.5%)

# Cross-domain: what types of artworks have provenance events in Amsterdam?
collection_stats(dimension="type", hasProvenance=true, location="Amsterdam")

# Top 30 depicted persons
collection_stats(dimension="depictedPerson", topN=30)

# Cross-filter with Iconclass or curated sets
collection_stats(dimension="type", iconclass="73D82")
# → what types of artworks depict the Crucifixion?

collection_stats(dimension="creator", collectionSet="Japanese prints", topN=20)
# → top 20 creators in the Japanese prints set

# Physical size distributions (binned by binWidth, in cm)
collection_stats(dimension="height", type="painting", binWidth=20)
# → height distribution of paintings in 20cm bins
```

Use `compact: true` on `search_artwork` only when you need the actual object numbers (e.g. to feed into `get_artwork_details`), not just the count or distribution. For pure counting, `collection_stats` is always more efficient.

### 2. Iconclass Research

Never pass iconographic concepts as free text to `search_artwork(iconclass=...)` —
it expects exact notation codes. Use the **Iconclass server** to discover codes first (see its SKILL file for full discovery workflows, FTS query patterns, hierarchy navigation, and cross-branch strategies).

**Quick reference — Iconclass server tools:**
- `search(query=...)` — keyword lookup ("crucifixion" → `73D6`)
- `search(semanticQuery=...)` — concept search ("domestic animals" → `34B1`)
- `browse(notation=...)` — hierarchy navigation (children, cross-refs, path)
- `resolve(notation=[...])` — batch lookup of known codes
- `expand_keys` / `search_prefix` — key variants and subtree enumeration

**Before handing off**, check `collectionCounts` — a code with 0 artworks returns nothing; a code with 2,000 vs 3 signals very different curatorial depth. **Never truncate Iconclass discovery queries** — use the default `maxResults` (25) or higher so you can evaluate all returned notations and their counts before deciding which codes to hand off.

**Searching with codes on this server:**
```
search_artwork(iconclass=["73D82"])
search_artwork(iconclass=["73D82", "25F33(DOVE)"])  # AND-combines across branches
```

Multiple codes AND-combine — this is how you express compound iconographic queries. If results are unexpected, use the Iconclass server's semantic search to discover alternative branches — the same concept can live in multiple places (a dog as pet under `34B11`, a dog as symbol of fidelity under `11A(DOG)`).

### 3. Century / Decade Wildcards

For longitudinal analysis, prefer `collection_stats` with decade or century binning. **Use `dateMatch: "midpoint"` for statistical comparisons** — the default `overlaps` mode double-counts broadly-dated objects across bins (see Critical Parameter Distinctions above):

```
collection_stats(dimension="decade", technique="etching", creationDateFrom=1500, creationDateTo=1800)
# → decade-by-decade etching counts in one call (default overlaps — inclusive but inflated)

collection_stats(dimension="decade", technique="etching", creationDateFrom=1500, creationDateTo=1800, binWidth=50)
# → half-century bins
```

For queries that need individual object numbers (not just counts), use `creationDate` wildcards with `search_artwork`:

```
search_artwork(technique="etching", creationDate="16*", dateMatch="midpoint")
search_artwork(technique="etching", creationDate="17*", dateMatch="midpoint")
# dateMatch="midpoint" ensures each artwork is counted in exactly one century
```

Supported patterns: `"1642"` (exact year), `"164*"` (decade), `"16*"` (century), `"1*"` (millennium).

### 4. Semantic Search + Structured Verification

Use `semantic_search` for concepts with no vocabulary term or Iconclass code —
atmosphere, emotion, cultural interpretation. Always follow up with a
structured `search_artwork` to test whether the same works are reachable
through controlled vocabulary.

```
semantic_search(query="loneliness and isolation in a vast empty space", type="painting")
# Review source text for each result — ask why it appeared
# Then: search_artwork(subject="landscape", curatorialNarrative="solitary")
# Compare overlap: works only reachable via semantic search are the interesting gap cases
```

**Pre-filtering:** `semantic_search` accepts all vocabulary filters from `search_artwork` as pre-filters — `subject`, `iconclass`, `depictedPerson`, `depictedPlace`, `productionPlace`, `collectionSet`, `aboutActor`, `imageAvailable`, `dateMatch`. These narrow the candidate set *before* semantic ranking, combining structured precision with conceptual search:

```
semantic_search(query="vanitas symbolism", subject="skull", type="painting")
# → paintings with skull subjects, ranked by how well they match "vanitas symbolism"
```

**Filter specificity matters.** Very broad single filters (e.g. `type: "print"` alone — 421K works, or `material: "paper"` — 643K) exceed the internal candidate limit, so semantic ranking operates on a subset. Results are still high-quality (distances are near-optimal), but a different subset might surface equally-good alternatives. For best coverage, combine two or more filters or pair a broad filter with a specific one (e.g. `type: "print", subject: "landscape"`).

**Language note**: English queries yield slightly higher precision against the
bilingual catalogue even though the embedding model is multilingual. If a Dutch
or German query returns unexpected results, reformulate in English.

### 5. Image Inspection and Overlay Placement

**Basic inspection + zoom is now a single step.** `inspect_artwork_image` automatically navigates the open viewer to the inspected region (`navigateViewer` defaults to `true`). No separate `navigate_viewer` call is needed for basic zoom — the viewer stays in sync with your analysis.

```
# Single call: inspect a region AND zoom the viewer there
inspect_artwork_image(objectNumber="SK-C-5", region="pct:70,60,20,20")
# → base64 image for AI analysis + viewer auto-zooms to the same region
```

**For overlays, use the inspect → overlay two-step with `relativeTo`:**

```
# Step 1: inspect the area
inspect_artwork_image(objectNumber="SK-C-5", region="pct:70,60,20,20")
# → found a detail at roughly the center of this crop

# Step 2: place overlay using crop-local coordinates — the server projects to full-image space
navigate_viewer(viewUUID=..., commands=[{
  action: "add_overlay",
  region: "pct:30,30,40,40",      # coordinates within the crop
  relativeTo: "pct:70,60,20,20",  # the crop region from Step 1
  label: "Detail"
}])
```

The `relativeTo` parameter eliminates manual coordinate conversion — specify where the feature is within the crop, and the server handles the projection. Both `region` and `relativeTo` must use `pct:` format.

Use `region="full"` for an initial composition overview before cropping to
details. `inspect_artwork_image` can surface content **absent from structured
metadata** — unsigned Japanese prints often have readable artist signatures,
publisher seals, and poem cartouches that the catalogue has not transcribed.

### 6. Provenance and Acquisition Research

Provenance research moves through three levels of detail:

1. **Scope and profile** — `search_artwork` with `provenance` (keyword) or
   `creditLine` for fast counts and facets. `creditLine` covers ~358K artworks
   (vs ~48K with parsed provenance) and captures the last link: how the museum
   acquired the work. Combine with `productionPlace` + `expandPlaceHierarchy`
   for geographic cross-tabulation.

2. **Structured chain analysis** — `search_provenance` for parsed chains with
   dates, prices, transfer types, and ownership periods. Add `facets: true`
   for quick distributional context alongside chain results.

3. **Single-object deep dive** — `search_provenance(objectNumber=...)` for the
   full chain, then `get_artwork_details` for narrative text and curatorial
   context.

**Cross-domain queries**: `hasProvenance: true` on `search_artwork` or
`collection_stats` bridges the two systems — e.g.
`collection_stats(dimension="type", hasProvenance=true)`.

**Enrichment transparency:** `categoryMethod` and `positionMethod` are
queryable input filters on `search_provenance`, not just output fields. When
results contain LLM-enriched records, the response includes a review URL.
**Always show this URL to the user.**

**Pagination**: when `totalResults` exceeds 50, paginate with `offset`
(increments of 50 until offset ≥ totalResults).

For the complete data model (AAM text format, transfer type vocabulary, party
roles, date and currency representations), tested query patterns (collector
profiling, wartime provenance, price history, acquisition channels, decade-level
time series), and enrichment methodology, see `references/provenance-patterns.md`.

### 7. Source–Copy Navigation

To trace reproductive prints back to their painted sources, use `productionRole`
to find copies, then search for the source artist's original:

```
search_artwork(productionRole="after painting by", creator="Rembrandt van Rijn")
# → get_artwork_details on a result to read its description (often names the source)
# → search_artwork(creator="Rembrandt van Rijn", type="painting", title="...") to find the source
# → get_artwork_image on both for side-by-side comparison
```

Note: `relatedObjects` currently returns an empty array (not available from the
vocabulary database). Use description text and title matching to navigate
between related works.

### 8. Collection Depth Assessment

For grant applications or scoping a research site:

```
collection_stats(dimension="creator", type="print", productionPlace="Japan", topN=20)
# → top 20 print artists from Japan + total count, in one call

collection_stats(dimension="decade", type="print", productionPlace="Japan")
# → temporal distribution

list_curated_sets(query="Japan")                                       # curatorial groupings
browse_set(setSpec="...")                                               # range of artists/dates
search_artwork(productionPlace="Japan", type="print", maxResults=10)  # sample works for closer inspection
```

### 9. Gender and Demographic Analysis

`creatorGender` is a modifier — always combine it with at least one content
filter. Use century wildcards to reveal structural patterns over time.

```
# Count women painters per century — use dateMatch="midpoint" to avoid double-counting
search_artwork(creatorGender="female", type="painting", creationDate="17*", dateMatch="midpoint", compact=true)
# Repeat for "18*", "19*"; then drop compact=true to identify leading artists per century

# Or: one-call gender breakdown per century
collection_stats(dimension="creatorGender", type="painting", creationDateFrom=1700, creationDateTo=1799)
# → male 4,521 (96.3%), female 48 (1.0%), ...
```

---

## Known Limitations and Fallbacks

| Issue | Workaround |
|---|---|
| `navigate_viewer` WebSocket disconnection | Use `inspect_artwork_image` as the reliable fallback for region analysis |
| No `subject` results in English | Try the Dutch term — vocabulary is bilingual ("fotograaf" not "photographer") |
| `semantic_search` skews toward prints/drawings | Filter with `type: "painting"` — prints and drawings outnumber paintings ~77:1 |
| `semantic_search` with very broad filter | Single broad filters (`type: "print"`, `material: "paper"`) exceed the candidate limit — results are good but not exhaustive. Combine filters for better coverage. |
| Inscription field empty in catalogue | Use `inspect_artwork_image` — AI vision can often read text directly from the image. Try `quality: "gray"` for better contrast on faded inscriptions or signatures. |
| `facets` on `search_artwork` | 11 dimensions: type, material, technique, century, creatorGender, rights, imageAvailable, creator, depictedPerson, depictedPlace, productionPlace. Configure with `facetLimit` (1–50, default 5). Pass `facets=true` for all or `facets=["creator","type"]` for specific ones. All entries include percentage. |
| `facets` on `search_provenance` | 5 dimensions: transferType, decade, location, transferCategory, partyPosition. Set `facets=true`. Percentages included. |
| Collection-wide distributions | Use `collection_stats` instead of `compact=true` counting loops — 21 dimensions across artwork and provenance domains with cross-domain filters (including `height`/`width` for physical size distributions, binned by `binWidth`). One call replaces N iterations. |
| `search_provenance` data model details | Batch prices, unsold lots, historical currencies, gap flags, parse methods, party coverage, 0-year durations, and `sortBy: "eventCount"` unreliability — see `references/provenance-patterns.md` |
| `attributionQualifier` + `creator` | Does not work — use `aboutActor` instead. See Critical Parameter Distinctions above. |
| `attributionQualifier: "manner of"` | Not a valid value — returns zero results. Use `aboutActor`. |
| Canonical artist name forms | Some artists use historical spellings (e.g. "Jheronimus Bosch"). If a known artist returns no results, check the canonical form via `get_artwork_details` on a known work. |

---

## Output Conventions

- Always report `objectNumber` (e.g. `SK-C-5`) linked to the Rijksmuseum's page for that artwork when surfacing works — it is the stable identifier across all tools
- Use `persistentId` (handle.net URI) for any citation or bibliographic reference
- When presenting multiple works, lead with the most significant first (the default importance ranking handles this for vocabulary queries)
- For image inspection findings, distinguish explicitly between what the structured metadata says and what the AI reads directly from the image — the distinction matters for research rigour

---
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