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제작자가 쓴 설명(영어 원문)
Search 14.5M Smithsonian Open Access objects, get CC0 images, find cross-collection connections.
이 세 가지는 절대 하나의 점수로 합치지 않아요. 의견, 측정, 인기는 서로 다른 것이니까요.
이 서버는 아직 쉬운 말로 된 요약을 쓰지 못했어요. 아래는 레지스트리에서 읽어 온 사실이에요.
쓰고 있는 AI 앱을 고른 다음 설정을 복사하세요.
SMITHSONIAN_API_KEY, MCP_LOG_LEVEL필요한 것
어떤 앱을 쓰세요?
위에서 앱을 고르세요. 기억해 두었다가 다음부터는 버튼에 “…에 설치”라고 표시해요.
웹, 데스크톱, 모바일의 Claude에서 쓸 수 있어요:
https://smithsonian.caseyjhand.com/mcpClaude Desktop → Settings → Developer → Edit Config를 열고 claude_desktop_config.json에 이 내용을 붙여 넣으세요
{
"mcpServers": {
"smithsonian": {
"command": "npx",
"args": [
"-y",
"@cyanheads/smithsonian-mcp-server"
],
"env": {
"SMITHSONIAN_API_KEY": "your-value",
"MCP_LOG_LEVEL": "your-value"
}
}
}
}터미널에서 이 명령어를 실행하세요
claude mcp add --transport http smithsonian https://smithsonian.caseyjhand.com/mcpclaude mcp add smithsonian --env SMITHSONIAN_API_KEY=your-value --env MCP_LOG_LEVEL=your-value -- npx -y @cyanheads/smithsonian-mcp-server실행하기 전에 빈 값을 채워 주세요.
프로젝트의 .cursor/mcp.json 또는 ~/.cursor/mcp.json에 붙여 넣으세요
{
"mcpServers": {
"smithsonian": {
"url": "https://smithsonian.caseyjhand.com/mcp"
}
}
}프로젝트의 .cursor/mcp.json 또는 ~/.cursor/mcp.json에 붙여 넣으세요
{
"mcpServers": {
"smithsonian": {
"command": "npx",
"args": [
"-y",
"@cyanheads/smithsonian-mcp-server"
],
"env": {
"SMITHSONIAN_API_KEY": "your-value",
"MCP_LOG_LEVEL": "your-value"
}
}
}
}브라우저가 앱을 열지 물어본 다음, 앱이 설정을 보여 주고 확인을 요청해요. 키 값은 비워 두니 직접 입력하세요.
프로젝트의 .vscode/mcp.json에 붙여 넣으세요
{
"servers": {
"smithsonian": {
"type": "http",
"url": "https://smithsonian.caseyjhand.com/mcp"
}
}
}프로젝트의 .vscode/mcp.json에 붙여 넣으세요
{
"servers": {
"smithsonian": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"@cyanheads/smithsonian-mcp-server"
],
"env": {
"SMITHSONIAN_API_KEY": "your-value",
"MCP_LOG_LEVEL": "your-value"
}
}
}
}브라우저가 앱을 열지 물어본 다음, 앱이 설정을 보여 주고 확인을 요청해요. 키 값은 비워 두니 직접 입력하세요.
ChatGPT는 인터넷에 있는 서버에만 연결할 수 있어요:
https://smithsonian.caseyjhand.com/mcp메뉴 이름은 요금제나 시기에 따라 조금 다를 수 있어요.
선택한 앱은 이 기기의 쿠키에만 저장돼요. 계정은 필요 없어요.
MCP_LOG_LEVEL env이 설정은 제작자의 레지스트리 정보로 만들었어요. README와 다르면 README를 따르세요.
도구 이름과 설명은 서버가 2026년 11월 6일에 알려 준 그대로예요. 우리가 아니라 제작자가 쓴 글이고, 일반 텍스트로만 보여 드려요.
smithsonian_search_objectsRecommended first step for open-ended or topic discovery: free-text search across 14.5 million Smithsonian objects, with optional exact filters. Filters narrow by museum unit, object type, indexed date term, culture, geographic place, subject topic, named party, and online/CC0 av
smithsonian_list_termsEnumerate the valid term vocabulary for an indexed Smithsonian filter field (unit_code, culture, place, date, online_media_type, topic). Terms are a controlled vocabulary — often plural or qualified (e.g. "Paintings", not "Painting") — so guessed filter values tend to return noth
smithsonian_get_objectFetch a normalized catalog metadata projection for a Smithsonian object by its record_id (from smithsonian_search_objects results). Returns title, dates, description, labeled notes, makers, materials, dimensions, places, cultures, topics, exhibitions, credit line, identifiers, ri
smithsonian_get_mediaReturn every CC0 (open-access) image for a Smithsonian object at multiple resolutions. The tool never returns an empty list — it names the reason instead: an object with nothing digitized, an object whose media is entirely non-image (3D models, scanned books, documents), and an o
smithsonian_browse_categoryBrowse Smithsonian objects within one exact category — a single museum (mode "museum"), culture, indexed date term (mode "period"), object type (mode "medium"), or subject term (mode "topic"). The value must be an exact indexed category term, not free text: resolve museum, cultur
smithsonian_find_relatedDiscover objects across Smithsonian collections related to a given anchor object, matched on shared metadata signals — culture, period, object type, named parties, and topic terms. Each related object is tagged with the signals that connected it to the anchor; a named-party signa
누구나 볼 수 있는 정보를 점검했고, 코드는 읽지 않았어요. 해당하지 않는 항목은 개수에서 뺐어요. · 2026년 11월 7일 점검
레지스트리 이름이 GitHub 계정 cyanheads 기준으로 확인되었고, 이 계정이 저장소를 소유하고 있어요
아직 GitHub에서 이 저장소를 읽어 오지 않았어요
아직 GitHub에서 이 저장소를 읽어 오지 않았어요
133ms 만에 응답(HTTP 200)
로그인 필요 없음
도구 설명 6개를 읽었고, AI에게 보내는 숨겨진 지시는 없었어요
레지스트리에서 이 카테고리의 활성 서버 전체와 비교했어요. 스타는 게시자가 저장소를 소유한 경우에만 세요.
해당하는 기본 점검 3개 중 3개를 통과했어요. 코드는 읽지 않았어요. 키나 토큰을 넣기 전에 서버가 어떤 권한을 요구하는지 확인하고, 꼭 필요한 권한만 있는 키를 쓰세요.
입력해야 할 값: SMITHSONIAN_API_KEY
둘 다 가능해요. 내 컴퓨터에 설치해도 되고, 클라우드 버전에 연결해도 돼요.
GitHub 저장소는 마지막으로 – 변경됐고, 최신 버전(0.4.0)은 5일 전 레지스트리에 올라왔어요.
Claude Desktop, Claude Code, Cursor, VS Code처럼 MCP를 지원하는 앱에서 쓸 수 있어요. URL로 연결되기 때문에, 원격 MCP 서버를 지원하는 앱(예: 개발자 모드의 ChatGPT)에서도 쓸 수 있어요.
같은 카테고리에는: Context7, Codebase Memory, Exa
같은 카테고리에서 다른 제작자가 만든 서버예요. 스타 순으로 정렬했어요.
카드 보는 법:★ GitHub 스타i세 기둥: 리뷰 · 점검 · 사용 정도i업데이트 = 최신 버전을 레지스트리에 공개한 날i
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제작자의 저장소에서 쓰인 그대로 가져왔어요. PopNx MCP가 검토하거나 보증한 내용이 아니에요. HTML, 이미지, 스크립트는 제거했어요. 링크를 누르면 이 사이트를 벗어나요.
@cyanheads/smithsonian-mcp-server Search 14.5M Smithsonian Open Access objects across 20+ museums via MCP, and retrieve CC0 images for the 5.2M that carry openly-licensed media. STDIO or Streamable HTTP. 6 Tools
Public Hosted Server: https://smithsonian.caseyjhand.com/mcp
The Smithsonian's Open Access catalog: 14.5 million objects across 20+ museums, 5.2 million with CC0 images. Search by free text or category, browse by museum, culture, date, or topic, and discover connections across collections.
| Tool | Description |
|---|---|
smithsonian_search_objects | Search across 14.5M objects by text query with optional filters (museum, type, date term, culture, place, topic, name, scientific name, media type, online-only, CC0). Returns curated summaries with total count. |
smithsonian_list_terms | Enumerate the valid term vocabulary for an indexed filter field (unit_code, culture, place, date, online_media_type, topic). Call before filtering to avoid empty results from invalid values; pass contains to resolve a guessed value to its exact term(s). unit_code terms come back with their museum names. |
smithsonian_get_object | Fetch a normalized catalog metadata projection for an object by ID: title, dates, materials, dimensions, exhibition history, credit line, and identifiers. |
smithsonian_get_media | Return all CC0-licensed images for an object at multiple resolutions (thumbnail, screen, high-res JPEG/TIFF). Only CC0 images returned, never an empty list — a distinct error reason names why, whether the object has nothing digitized, only non-image media, or only restricted images. |
smithsonian_browse_category | Browse objects within one exact category (museum, culture, period, medium, topic) with total count, a page of objects, and museum breakdown. Requires an exact indexed category term. |
smithsonian_find_related | Discover cross-collection objects related to an anchor, matched on shared culture, named-party, topic, and period signals. |
| Skill | Description |
|---|---|
smithsonian-guide | Gates for agents querying the catalog: query syntax (OR never widens a search), counting a museum's or a place's holdings, finding a person's indexed name, genus-level scientific names, and mirror-served pages |
Ships in skills/ for plugin installs, the npm package, the Docker image, and the .mcpb bundle, and is served over MCP as skill://smithsonian-guide/SKILL.md.
smithsonian_search_objects toolAND constraints. Plain words and quoted phrases keep their matching semantics; active Lucene expressions are grouped to preserve Boolean precedence.filters.online_media_type accepts exact smithsonian_list_terms media categories such as Images, 3D Models, and Sound recordings. These describe the index; even Images does not guarantee attached or CC0 images. Check smithsonian_get_object.media_summary for deliverability. Media categories feed search, with no media-type browse mode.1920s, 500-1500, 21st century, -2500), culture, geographic place, subject topic, named party (name), scientific name (scientific_name), indexed media category (online_media_type), online-only, CC0-onlytopic and name are hard indexed constraints, not free text — topic: "Quilts" matches 1,134 objects where the bare word matches 2,677, and name: "Warhol, Andy" matches 421 against 715record_id, plus holding_museum_name when the holding unit differs from the indexed unit_code (the 3D digitization scans)filters key is rejected by namestart + rows for standard pagination (offset-based, default 20, up to 1,000 per page — a larger rows is served at 1,000 with a notice). When objects remain, the notice names the next startsmithsonian_list_terms toolunit_code, culture, place, date, online_media_type, topic — a controlled vocabulary, often plural (e.g. Paintings, not Painting); ground filter values here before searching or browsingcontains filters the vocabulary by a case-insensitive substring — resolves a guessed value (e.g. greek → Greek, Attic) in one call; for unit_code it also matches museum names (National Air and Space → NASM), and each unit_code term comes back with a labels map of its museum namestart + rows (default 50, up to 40,000 per page — a larger rows is served at 40,000 with a notice); topic (133k terms) and place (114k) are large enough to pair with containsSMITHSONIAN_TERMS_CACHE_TTL_SECONDS (default 3600 seconds, 0 disables) — upstream ignores paging and returns the whole set on every call, so paging a large vocabulary uncached re-downloads it each timeobject_type is not enumerable here — harvest its values from the object_type field in smithsonian_search_objects resultssmithsonian_get_object toolrecord_id from smithsonian_search_objects — do not construct IDs manuallysmithsonian_get_media for full image URLs. media_summary.non_image_media[] lists every media item that is not an image (3D models, scanned books, documents) as { type, url?, is_cc0 }, in catalog orderholding_museum_name names the holding unit when it differs from unit_code, the code the record is indexed under (the 3D digitization scans); the text output renders it as Held bydescription selects recognized prose labels; administrative notes stay labeled.scientific_names, reported taxonomy ranks, and valid decimal collection coordinates with their own locality when present. The arrays are empty when no names or valid points are available; absent ranks are omitted. No accepted name, geocoding, or accuracy is inferred. Use scientific_names as exact filters.scientific_name values in search; this field is not enumerable via smithsonian_list_terms.smithsonian_get_media toolno_media (nothing digitized), no_images (media exists but is entirely non-image — 3D models, scanned books, documents; the recovery hint names the types present and points to smithsonian_get_object's media_summary.non_image_media, which links each item), not_cc0 (images exist but none are CC0, thrown as Forbidden)smithsonian_search_objects with filters.cc0_only: true to find objects with downloadable CC0 images before calling this toolsmithsonian_browse_category toolmuseum (by unit code, e.g. "NASM" — matched exactly, not by museum name), culture (e.g. "Aztecs"), period (indexed date term, e.g. "1940s" or "500-1500"), medium (object type, e.g. "Paintings"), topic (subject term, e.g. "Quilts")value must be an exact indexed category term — resolve museum, culture, period, and topic vocabulary with smithsonian_list_terms first; object_type is not enumerable there, so harvest it from smithsonian_search_objects resultsstart + rows for standard pagination (offset-based, default 10, up to 1,000 per page — a larger rows is served at 1,000 with a notice). When objects remain, the notice names the next start — adjacent pages retrieve the objects a capped sample omitsinvalid_category with a mode-specific recovery hint: a value outside the vocabulary gets the exact smithsonian_list_terms call that resolves it, and a value the index enumerates but that matches no objects is named as such and routed elsewheresmithsonian_search_objects instead — this tool requires an exact category valuesmithsonian_find_related toolmaker, collector, donor, issuing authority, …) rather than a fixed maker label, prefers the indexed name facet as a hard filter when the record has one, and is dropped when its value only repeats the culture signaltopic: filter, so every object it tags carries that subject term rather than merely mentioning the wordsimilarity_signals on each result names every metadata term that conne앞부분만 보여 드려요. 나머지는 저장소에서 읽어 보세요