---

name: museum-search description: Search the Met Museum Open Access Paintings collection using semantic search, find similar artworks, and search by image. Use this when users ask about art, paintings, museum collections, or want to find artworks by description, visual similarity, or artist.

Met Museum Paintings Semantic Search

Search ~2,300 paintings from the Met Museum Open Access collection using AI-powered semantic search.

Keywords: art search, museum, paintings, Met Museum, semantic search, artwork discovery, visual similarity, art history

When to Use This Skill

Use this skill when users:

• Ask to find artworks by description ("find paintings of people on bridges")
• Want to explore museum collections
• Ask about specific artworks or artists at the Met
• Want to find visually similar artworks
• Upload an image to find matching paintings

API Base URL

Copyhttps://museum-semantic-search.vercel.app/api/mcp

API Endpoints

POST /search - Search Artworks

Search the collection using text queries with optional filters.

Copycurl -X POST https://museum-semantic-search.vercel.app/api/mcp/search \
  -H "Content-Type: application/json" \
  -d '{
    "query": "person with a dog",
    "mode": "semantic",
    "limit": 10
  }'

Request body:

Copy{
  "query": "string (required)",
  "mode": "hybrid | semantic | keyword",
  "detail": "minimal | standard | full",
  "filters": {
    "artistName": "string (fuzzy matched)",
    "yearStart": 1800,
    "yearEnd": 1900,
    "department": "string (exact match)",
    "culture": "string (exact match)",
    "classification": "string (exact match)",
    "tags": ["string (exact match)"],
    "medium": "string (fuzzy matched)",
    "country": "string (exact match)",
    "onView": true,
    "isPublicDomain": true
  },
  "limit": 10,
  "offset": 0
}

Mode selection:

• "hybrid" (default, recommended): Combines keyword + semantic search. Best for most queries.
• "semantic": For purely conceptual/descriptive queries like "lonely figure in nature", "woman looking in mirror", "stormy seascape"
• "keyword": For exact matches like artist names or specific titles

Detail levels:

• "minimal": Just id, score, title, artist, date (fastest)
• "standard" (default): + medium, classification, department, tags, culture, image_url, source_url, alt_text (concise AI description)
• "full": + long_description (detailed AI description), similar_artworks, dimensions, etc. Use when you need to verify semantic content.

Response:

Copy{
  "meta": {
    "query": "person with a dog",
    "mode": "hybrid",
    "total": 150,
    "returned": 10,
    "offset": 0,
    "limit": 10,
    "has_more": true,
    "filters_applied": {},
    "processing_time_ms": 234
  },
  "results": [
    {
      "id": "met_436524",
      "score": 0.85,
      "title": "A Boy with a Flying Squirrel",
      "artist": "John Singleton Copley",
      "date": "1765",
      "medium": "Oil on canvas",
      "classification": "Paintings",
      "department": "American Wing",
      "image_url": "https://images.metmuseum.org/...",
      "thumbnail_url": "https://images.metmuseum.org/.../small/...",
      "tags": ["Boys", "Portraits", "Squirrels"],
      "culture": "American",
      "source_url": "https://www.metmuseum.org/art/collection/search/436524",
      "alt_text": "Portrait of a young boy in blue satin holding a gold chain attached to a pet flying squirrel on a polished table"
    }
  ]
}

GET /artwork/{id} - Get Artwork Details

Get complete details for a specific artwork.

Copycurl https://museum-semantic-search.vercel.app/api/mcp/artwork/met_436524

Response includes:

• Basic info: title, titles (alternate), artist, artists (with roles, bio, dates)
• Dates: date, date_start, date_end
• Physical: medium, dimensions
• Classification: classification, department, object_name
• Context: culture, period, dynasty, country, region
• Collection: collection, collection_id, credit_line, accession_year
• Status: is_public_domain, is_highlight, on_view, gallery_number
• Image: url, thumbnail_url, width, height
• AI description: alt_text, long_description, emoji_summary
• Tags: array of keywords
• Similar artworks: id, similarity_type, confidence, explanation
• Links: source_url (Met Museum page)

GET /similar/{id} - Find Similar Artworks

Find artworks similar to a given artwork using different methods.

Copy# Precomputed (LLM-curated, recommended - includes explanations)
curl "https://museum-semantic-search.vercel.app/api/mcp/similar/met_436524?method=precomputed&limit=10"

# Embedding-based visual similarity (CLIP)
curl "https://museum-semantic-search.vercel.app/api/mcp/similar/met_436524?method=embedding&model=jina_clip&limit=10"

# Embedding-based conceptual similarity (text)
curl "https://museum-semantic-search.vercel.app/api/mcp/similar/met_436524?method=embedding&model=jina_text&limit=10"

# Metadata-based (same artist, period, culture, etc.)
curl "https://museum-semantic-search.vercel.app/api/mcp/similar/met_436524?method=metadata&limit=10"

# Combined (fuses metadata + text + image embeddings)
curl "https://museum-semantic-search.vercel.app/api/mcp/similar/met_436524?method=combined&limit=10"

Query parameters:

• method: "precomputed" (default) or "embedding"
• model: "jina_clip" (visual) or "jina_text" (conceptual) - only for embedding method
• limit: 1-50 (default 10)

Response:

Copy{
  "meta": {
    "source_artwork": {
      "id": "met_436524",
      "title": "...",
      "artist": "..."
    },
    "method": "precomputed",
    "model": null,
    "total": 10,
    "returned": 10,
    "processing_time_ms": 45
  },
  "results": [
    {
      "id": "met_437234",
      "score": 0.92,
      "title": "...",
      "artist": "...",
      "similarity_type": "compositional",
      "similarity_explanation": "Both feature a central figure with a dog..."
    }
  ]
}

POST /image-search - Search by Image

Find visually similar artworks by uploading an image.

Copycurl -X POST https://museum-semantic-search.vercel.app/api/mcp/image-search \
  -H "Content-Type: application/json" \
  -d '{
    "image": "data:image/jpeg;base64,/9j/4AAQ...",
    "limit": 10
  }'

Request body:

Copy{
  "image": "base64-encoded image (with or without data URL prefix)",
  "mimeType": "image/jpeg",
  "filters": { },
  "limit": 10,
  "detail": "standard"
}

Response: Same format as /search but includes embedding_time_ms in meta.

GET /filters - Get Filter Options

Get available filter values for the collection. Supports search mode to find specific filter values.

Copy# Get all top filter values
curl https://museum-semantic-search.vercel.app/api/mcp/filters

# Search for specific filter values (case-insensitive)
curl "https://museum-semantic-search.vercel.app/api/mcp/filters?search=japan"
curl "https://museum-semantic-search.vercel.app/api/mcp/filters?search=oil&field=mediums"

Query parameters (for search mode):

• search: Case-insensitive search string (e.g., "japan", "portrait", "oil")
• field: Limit to specific field: departments, classifications, cultures, mediums, tags, countries, periods
• limit: Max results per field (1-100, default 20)

Response (default mode):

Copy{
  "departments": [{ "value": "European Paintings", "count": 1500 }, ...],
  "classifications": [{ "value": "Paintings", "count": 2300 }, ...],
  "cultures": [{ "value": "French", "count": 450 }, ...],
  "mediums": [{ "value": "Oil on canvas", "count": 1800 }, ...],
  "tags": [{ "value": "Portraits", "count": 800 }, ...],
  "date_range": { "min": 1250, "max": 1950 },
  "schema": {
    "artistName": { "type": "string", "description": "...", "examples": [...] },
    "medium": { "type": "string", "description": "...", "examples": [...] },
    "yearStart": { "type": "number", "description": "...", "min": 1250, "max": 1950 },
    ...
  }
}

Response (search mode):

Copy{
  "search_query": "japan",
  "field_filter": "all",
  "results": {
    "cultures": [
      { "value": "Japan", "count": 45 },
      { "value": "probably Japan", "count": 12 }
    ]
  }
}

Workflow

Step 1: Discover the Collection

Call GET /filters first to see available departments, cultures, mediums, tags, and date ranges. Use search mode to find specific filter values.

Step 2: Search

Use POST /search - hybrid mode (default) works well for most queries.

Step 3: Get Details

Call GET /artwork/{id} on interesting results, or use detail: "full" in search to get complete details inline.

Step 4: Explore

Use GET /similar/{id} to discover related works.

Example Queries

Tips

• The collection is primarily European and American paintings from the Met Museum
• AI-generated descriptions enable finding artworks by what's depicted, not just metadata
• Use source_url to link users to the official Met Museum page
• Pagination: when has_more is true, increase offset to get more results
• For similar artworks: "precomputed" gives the best quality with explanations; "embedding" is faster
• No authentication required - the API is publicly accessible