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Navigate to and extract content from specific web URLs. Good for reading data portals, documentation, and landing pages.
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Basado en la clasificación ocupacional SOC
| description | Navigate to and extract content from specific web URLs. Good for reading data portals, documentation, and landing pages. |
| name | web_browse |
| source | built-in |
| tags | ["browse","web","scraping"] |
| tools | ["web_browse"] |
Apply when you have a specific URL that you need to read in full — a data portal, regulation document, methodology page, research paper landing page, or any page returned by a web_search that warrants deeper reading. Use after web_search has identified promising sources. Do not use to discover URLs (use web_search for that).
Produce a structured note in the proof field with: source URL, page title, publishing organisation, date retrieved, extracted key facts with verbatim quotes, and any follow-up URLs identified for further inspection.
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Execute arbitrary Python code in an isolated subprocess. Use for data transformation, statistical analysis, geospatial processing, knowledge graph modeling, ontology engineering, RDF serialization, data visualization, document parsing, and any computation task. Always print() results to capture them.
Download a dataset from a URL and inspect its actual contents — schema, column types, shape, sample rows, and null counts. Supports CSV, Parquet, Excel, JSON, and JSONL. Use this to look inside a dataset, not just its landing page.
Download a PDF from a URL and extract its full text — ideal for research papers, reports, and documentation. Returns page count and extracted text.