mcp server

Provenio — Art Intelligence

Art provenance intelligence — 282K-node knowledge graph with cited answers and honest gaps.

Description as published by the maintainer. Source

• version 0.4.2
• active
• memory and context

active — Registry entry last updated 2026-07-19. Dashed tags are derived by ZBS Index from the published description, not stated by the maintainer.

What this server can do

27 functions, named and described by the server itself. Parameter names are shown because they say more about what a function does than its name usually does.

byok_external_query(method, body_json, timeout_ms, endpoint_url)

Cost: ~variable. Forward a JSON HTTP request to YOUR own external API endpoint and return the response. Use when: you need to query a third-party art-market source (Artnet/Artprice/your own gallery DB) that we do not host. Auth: pass your API key in MCP request header 'x-byok-auth' — we forward it as Authorization to your endpoint. Allowed hosts: must match an HTTPS URL on the public internet (no internal IPs, localhost, file://, etc). Returns: {status, body, headers, fetched_at, source_url} — body parsed if JSON, raw if not. Logged for your own audit trail; never trained on. We do NOT cache or persist response bodies. Required: endpoint_url.

compare_career_patterns(artist_id_1, artist_id_2)

Cost: ~4s. Side-by-side career trajectory comparison for two artists. Returns full trajectory data for each + a structured comparison: pattern overlap score, career stage delta, exhibition/market delta, interpretation, and recommendation. Use when: gallery wants to pitch an emerging artist by showing they mirror a validated comparable's early career. Use when: curator needs narrative evidence that artist A is at the same inflection point artist B was at in year X. Do NOT use when: you need data on one artist — use get_career_trajectory instead. Do NOT use when: you want to discover emerging artists — use find_emerging_artists_by_pattern instead. Required: artist_id_1, artist_id_2.

fetch(id)

Fetch the full Provenio record for an id returned by `search` (person:… or artwork:…). Returns {id, title, text, url, metadata} where text is a readable provenance/market/influence summary. (ChatGPT connector document-fetch tool — wraps get_artist / get_artwork.) Required: id.

find_comparable_artworks(limit, period, artist_id, artwork_id, years_back, patron_class, medium_keyword, iconographic_subject)

Cost: ~2s. Comparable set. EITHER artwork_id seed → similar works, OR artist_id seed → peer artists. Not for same-artist lot history (use search_auction_history).

find_emerging_artists_by_pattern(limit, pattern, min_confidence, birth_year_after)

Cost: ~3s. Discover emerging artists whose current KG footprint matches a historical career pattern. Patterns: A=institutional_escalation, B=curator_championing, C=movement_anchor, D=late_market_discovery, E=diaspora_rediscovery. Returns ranked candidates with confidence score, exhibition/market data, and signal summary. Use when: gallery wants a shortlist of artists at an early inflection point matching a proven pattern. Use when: curator is building a thematic exhibition and wants artists at the right career stage. Use when: collector wants names not yet on the market radar. Do NOT use when: you already have a specific artist in mind — use get_career_trajectory instead. Do NOT use when: you want to compare two artists — use compare_career_patterns instead. Coverage note: results depend on exhibition data ingested; artists with no exhibition records won't appear. Required: pattern.

find_galleries_by_artist(artist_id, include_former)

Cost: ~1s. Find all galleries that represent (currently or formerly) a specific artist. Returns: ordered list of representations (current first, then deceased_estate, then former), with gallery tier/city/country and period. Use when: verifying primary-market representation for valuation or attribution context. Use when: building artist-centric outreach (which galleries control supply). Use when: tracking representation history for a deceased artist's estate. Required: artist_id.

get_artist(artist_id)

Cost: ~1s. Composite artist profile — identity + reception + 1-hop influence in/out + education + market aggregates. SINGLE CALL USUALLY SUFFICIENT. Use when: you have an artist ID and need their full picture. Do NOT chain with get_influence_network unless you need depth > 1. Do NOT use for multi-artist comparison — use query_market_summary. Required: artist_id.

get_artwork(artwork_id)

Cost: ~1s. Composite artwork profile — metadata + Panofsky subjects + reception + custody + polity + transactions. SINGLE CALL USUALLY SUFFICIENT. Use when: you have an artwork ID. Do NOT chain with get_provenance_chain unless you need gap-risk flags specifically. Required: artwork_id.

get_career_trajectory(artist_id, include_comparables)

Cost: ~2s. Artist career trajectory analysis: milestones, career stage, pattern detection (A–E), and comparable historical artists who followed the same path. Career stages: PRE_MARKET → REGIONAL → CRITICAL_PHASE → MARKET_ENTRY → ESTABLISHED. Patterns: A=institutional escalation, B=curator championing, C=movement anchor, D=late market discovery, E=diaspora rediscovery. Use for: emerging artist discovery, gallery acquisition decisions, curator narrative building. Do NOT use for purely biographical queries — use get_artist instead. Required: artist_id.

get_exhibition_history(artist_id, artwork_id)

Cost: ~0.5s–1s. Exhibition history for an artist OR artwork. SINGLE CALL. Returns normalised exhibition records (exhibitions table) when available; falls back to knowledge-graph event nodes (raw_json scan) when the normalised table is empty. Coverage: 94 curated events for 20+ artists as of 2026-04-24. For artists with no data, returns actionable guidance including the exact CLI command to run artsy_exhibitions.py to ingest missing records. Always returns coverage_gaps and suggested_next_tools when data is sparse or absent.

get_gallery_artists(gallery_id, include_former)

Cost: ~1s. Get the represented-artist roster for a specific gallery. Returns: gallery metadata + ordered list of artists with exclusivity (exclusive/shared/former/deceased_estate), period, notes. Use when: preparing pitch context for a specific gallery (cold email, fair planning). Use when: validating whether artist representation overlap exists between two galleries. Do NOT use when: you only have the artist name — use find_galleries_by_artist instead. Required: gallery_id.

get_influence_network(direction, max_depth, seed_person_id)

Cost: ~2-3s. N-hop influence BFS. Use ONLY when depth>1 needed — get_artist already has 1-hop. Required: seed_person_id.

get_lineage_cluster(artist_id, include_students, include_teachers)

Cost: ~1s. Formal teacher/student cluster (education_lineage). Use for academic lineage. For conceptual influence: get_influence_network. Required: artist_id.

get_movement_context(limit, concept_id, cross_tradition)

Cost: ~0.5s. Concept node + linked artworks. Pass cross_tradition=true for hasFunctionalAnalog concepts (lotus↔rose). SINGLE CALL USUALLY SUFFICIENT. Use when: definitional context or iconography cluster. Do NOT use for artwork filters — use search_artworks. Required: concept_id.

get_provenance_chain(artwork_id, period_end, period_start)

Cost: ~1s. Custody timeline + gap-risk flags (Nazi, colonial, Russian Rev, Knoedler 1970-2009). Use only for deep due diligence — get_artwork already has basic custody. Required: artwork_id.

get_reception_arc(artist_id, artwork_id)

Cost: ~1s. Time-ordered reception claims for artist or artwork. Use for valence-shift narrative. Use query_reception_price_correlation if you also need price.

lookup_europeana(limit, query)

Cost: ~0.5s. Search the Europeana cultural heritage corpus (~50M records from EU museums/archives) for art records. Use when: triangulating Provenio data against European museum holdings, or finding object records with images for a Western European artist. Returns: {total, items: [{title, creator, dataProvider, year, image_url, edm_url}]} Source: api.europeana.eu (public-domain Europe gateway, demo key embedded; users can supply own key via byok_external_query for high-volume). Required: query.

lookup_met_museum(limit, query, object_id, has_images)

Cost: ~0.3s. Search the Metropolitan Museum of Art collection (free public API · ~480K objects) and optionally dereference an objectID. Use when: you need a museum-confirmed match for an artist or work, or want to triangulate Provenio data against Met holdings. Returns either {total, objectIDs[]} for a search OR a full object record if object_id supplied. Source: https://collectionapi.metmuseum.org/public/collection/v1 · zero PII, public domain images flagged.

lookup_wikidata_art(sparql)

Cost: ~3-8s. Run a SPARQL query against Wikidata (public, no auth) for art-related entities. Use when: you need cross-museum location data for an artist's works, or biographical data not in Provenio. Pattern: pass a SPARQL query string. Example below to get all Klimt artworks with current location. Returns: {total, bindings: [...]} — raw SPARQL JSON results format. Source: https://query.wikidata.org/sparql · 12-second SPARQL query timeout enforced. Required: sparql.

query_market_summary(era, limit, polity, artist_id, min_price_usd, reception_register)

Cost: ~2s. Per-artist market aggregates × reception × polity. Use for cross-artist comparison. Single artist deep profile: get_artist. Individual lots: search_auction_history.

query_polity_dispersal(polity_id, years_after_collapse, rank_by_claim_priority)

Cost: ~2s. Polity-origin artworks now dispersed. Use for restitution/cultural-heritage analysis. For simple list: search_artworks. Required: polity_id.

query_reception_price_correlation(artist_id)

Cost: ~2s. Reception × price timeline for one artist. Combines get_reception_arc + price trend in one call. Required: artist_id.

search(query)

Search the Provenio art-provenance knowledge graph (artists + artworks) by free text. Returns a ranked list of {id, title, url}. Pass an id to `fetch` for the full record. Use this as the entry point for any name/title lookup. (ChatGPT connector entry tool — wraps search_artists + search_artworks.) Required: query.

search_artists(era, limit, query, nationality)

Cost: ~0.4s. Shortlist person nodes by name/nationality/era. Returns [{id, display_name, birth_year, death_year, nationality, era}]. Use when: you have a name but no person ID. Try a partial name if exact match fails (e.g. 'Basquiat' not 'Jean-Michel Basquiat'). Do NOT use if: you already know the ID — call get_artist directly (one call). Returns person IDs in 'person:slug' format — pass these directly as the 'artist' parameter in search_artworks. STOP after this unless you need a specific artist's full profile. Required: query.

search_artworks(limit, query, artist, medium, date_to, date_from, provenance_risk, attribution_status)

Cost: ~0.5s. Shortlist artworks by title/artist/medium/date, or risk-filter (provenance_risk, attribution_status). Returns summary rows. Use when: you need a list, not one work's detail. Do NOT use if: you know the artwork ID — call get_artwork (one call contains iconography + custody + polity + transactions). Do NOT chain multiple search_artworks calls for the same work — use get_artwork after you have the ID. Do NOT put artist names in the query field alone — use the artist filter with the person ID slug (e.g. 'person:jean-michel-basquiat'). If title is ambiguous, combine query + artist filter. Title match is substring (ILIKE %query%). For best results, use the artist filter rather than embedding artist name in query. Note: when provenance_risk filter is used, returns coverage_gaps: ['no_custody_history'] when the matching custody record pool is smaller than expected — interpret this as a data-density limitation, not a clean result. STOP after this unless you need deep detail on ONE artwork. Required: query.

search_auction_history(limit, date_to, date_from, artwork_id, artist_name, min_price_usd, medium_keyword, percentile_summary)

Cost: ~0.7s. Auction transaction rows with optional percentile_summary=true to also return P25/50/75/90 + realized-vs-estimate in ONE call. Use when: need lot-level prices OR percentile stats. Do NOT use if: you only need per-artist aggregates — use query_market_summary. SINGLE-CALL FRIENDLY — combine filters + percentile_summary for appraisal work.

search_galleries(city, tier, limit, query, country)

Cost: ~1s. Search galleries by name, tier, country, or city. Tiers: mega (Gagosian/Zwirner level), major (international program), boutique (focused), emerging (newer). Use when: identifying which galleries operate in a specific market or tier band. Use when: building a B2B target list (galleries by region/tier). Do NOT use when: you want to know which gallery represents a specific artist — use find_galleries_by_artist instead.

Last successful function declaration observed on 2026-08-13. Source: https://provenio.art/api/mcp. We list what the server declared; we do not call any of these functions.

Endpoint status observed on 2026-08-13. Source: https://provenio.art/api/mcp.

Signals

These are separate measurements of different things. They are deliberately not combined into one score, because a popularity number that mixes website traffic with saves and stars cannot be checked or acted on.

Where to get it

• Repository — https://github.com/mildo-ai/provenio
• Remote endpoint (streamable-http) — https://provenio.art/api/mcp