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Dataset env Public data APIs CC0 1.0 (collection data) capture-browser-scenario

ArticCollectionEnv-REA

ArticCollectionEnv-REA is an AgentEnv replay environment for Art Institute of Chicago API: Art museum collection (artworks, artists, galleries, exhibitions). Built with REA, published as a Hugging Face dataset, practiced via MCP tools — this page is a teaching mirror on rea.run, not the runtime.

Numbers and fields are cited from the Hugging Face note / catalog snapshot.

Open on Hugging Face

Datasets and large HAR/parquet stay on HF. This page is a teaching mirror only — no runtime here.

Open HF dataset ↗

Recorded target

Product: Art Institute of Chicago API

Version: v1

License: CC0 1.0 (collection data)

Hosting: public API, real records

Capture: REA's capture-browser-scenario

Updated: Oct 19, 2026 (Asia/Shanghai)

AgentEnv metrics

MCP tools: 11

Retrieval tasks: 12

Downloads (HF snapshot): 66

Likes: 0

id: earakely-scale/ArticCollectionEnv-REA

What this is

ArticCollectionEnv-REA is an AgentEnv replay environment for Art Institute of Chicago API. Scenario: Art museum collection (artworks, artists, galleries, exhibitions). Teaching mirror on rea.run — practice keywords: AgentEnv, REA, replay environment, MCP tools.

  • Product: Art Institute of Chicago API
  • Scenario: Art museum collection (artworks, artists, galleries, exhibitions)
  • Category: Public data APIs

What REA captured

  • Capture form: REA's capture-browser-scenario (note: Captured by REA's capture-browser-scenario).
  • Target product / version: Art Institute of Chicago API v1.
  • License (HF note): CC0 1.0 (collection data).
  • Hosting / data shape: public API, real records.
  • Large HAR/parquet files live in the Hugging Face dataset — not on these Pages.

What you practice in AgentEnv

This env exposes about 11 MCP tools and 12 retrieval tasks (HF note snapshot). Retrieval tasks: the agent uses MCP tools to find or act on information inside the replay environment; the baseline table shows published model scores. How to read baselines: scores look like a/b — a = tasks correct, b = task count; cross-check with the task total above. Practice runs on AgentEnv / HF, not on rea.run.

Metric Value (HF note)
MCP tools 11
Retrieval tasks 12

Model baselines

Scores cited from the HF note snapshot (retrieval-task scores). How to read: numerator = tasks correct, denominator = task count.

Model Score
gpt-oss-120b 11/12
GLM-5.3-Flash 12/12

Relation to REA

Pipeline: REA captures an authorized browser/API session → published as a Hugging Face dataset → replayed as MCP tools on Scale AgentEnv. rea.run provides the categorized catalog and teaching copy — it is not the runtime and does not host capture files.

  1. 1

    Authorize the target (own / OSS / written permission / approved lab).

  2. 2

    Capture the browser session with REA (HAR or capture-browser-scenario).

  3. 3

    Replay as AgentEnv MCP tools on Scale AgentEnv / the HF space — not on rea.run.

License & compliance

License (cited from HF note): CC0 1.0 (collection data). Authorized use only: own systems, OSS, written permission, or an approved lab. Do not record or replay against unauthorized production targets. This site does not provide cracking, bypass, or unauthorized-access guidance.

Compliance · Legal boundary

Next steps

Install AgentEnv / REA

To record sessions locally with REA, install rea-agents from official sources. AgentEnv replay and eval run on Scale / HF — not on this site.

HF note (cited)

Snapshot text from the catalog note — not a verbatim dump of a long HF card.

Public data APIs · Art Institute of Chicago API v1 (CC0 1.0 (collection data)), public API, real records. Art museum collection (artworks, artists, galleries, exhibitions). 11 tools, 12 retrieval tasks; gpt-oss-120b 11/12, GLM-5.3-Flash 12/12. Captured by REA's capture-browser-scenario.

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