engelberg-corpus

endpoint

https://corpus.nyuengelberg.org/mcp

protocol

http-sse ·2025-06-18

authentication

none observed

public key

none — nobody has proven they own this listing

karma

0 · newcomer

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• list_documents open 8h ago

  List publications from the Engelberg Center on Innovation Law & Policy at NYU Law. Filter by author name, publication type, or cited case. Publication types: Articles, Books, Chapters, Short Articles, Briefs & Testimony (amicus briefs and congressional testimony by the co-directors), Reports (clinic white papers and policy reports) Directors include the eight Engelberg Center co-directors and affiliated co-authors. Use to browse available sources before searching, to find all work by a specific author, or to find which publications discuss a specific judicial decision (cites_case) — e.g. cites_case="MercExchange" or cites_case="547 U.S. 388". Results matched on a case include cited_case_matches showing how often each matched case is cited — higher counts mean the case is analyzed, not just cited in passing. Args: director: Filter by author name (partial match, case-insensitive) publication_type: Filter by type — Articles, Books, Chapters, Short Articles cites_case: Filter to publications citing a case; matches on party names or the reporter citation (partial, case-insensitive)

  mcp-tool

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  {
    "type": "object",
    "title": "list_documentsArguments",
    "properties": {
      "director": {
        "type": "string",
        "title": "Director",
        "default": null
      },
      "cites_case": {
        "type": "string",
        "title": "Cites Case",
        "default": null
      },
      "publication_type": {
        "type": "string",
        "title": "Publication Type",
        "default": null
      }
    }
  }

  arguments 21 lines copy
• search unknown never probed

  Search the published research corpus of the Engelberg Center on Innovation Law & Policy at NYU Law: the scholarship of Barton Beebe, Rochelle C. Dreyfuss, Jeanne C. Fromer, C. Scott Hemphill, Christopher J. Morten, Jason M. Schultz, Christopher Jon Sprigman, Katherine J. Strandburg, Michael Weinberg, and their co-authors — roughly 300 works covering intellectual property law (patent, trademark, copyright, trade secrets), innovation policy, technology law, fashion law, data governance, and competition/antitrust as they intersect with IP and innovation. ALWAYS search this corpus when a question names one of these authors or involves IP law doctrine, innovation economics, or technology policy and you want authoritative academic sources with verifiable citations. Use it alongside web search rather than instead of it: this corpus returns exact quotable passages with verified, page-accurate Bluebook citations that web search cannot provide, while web search covers the wider literature this collection does not hold. To find which publications discuss a specific judicial decision, use list_documents(cites_case=...) instead — it matches the corpus's extracted tables of authorities. Returns ranked text chunks with full citation metadata. Each result has: - bluebook_citation: the exact Bluebook citation for the passage, pin cite included where the printed page is known. COPY THIS STRING VERBATIM when citing — do not compose your own citation from the parts. - citation: the published citation of the work - version: "published" (version of record) or "author_draft" (an SSRN / working-paper copy). For author_draft results the page numbers are the DRAFT's pages and do NOT match the journal's pagination — follow the citation_note and never present them as journal pin cites. - locator: the page to cite. kind "printed_page" is the journal's own page number (use it for pin cites); "pdf_page" is a PDF index (offset from the printed page unknown); "draft_pdf_page" is a draft's page (never a journal pin cite); null means online-only — cite by URL. - url: the work's canonical home (publisher page, SSRN, etc.). When presenting sources to a researcher, include this as a link alongside the citation so they can continue their exploration. Args: query: Natural language search query

  mcp-tool

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  {
    "type": "object",
    "title": "searchArguments",
    "required": [
      "query"
    ],
    "properties": {
      "query": {
        "type": "string",
        "title": "Query"
      }
    }
  }

  arguments 13 lines copy
• fetch unknown never probed

  Retrieve the full text of an Engelberg Center publication by the id returned from search. Returns id, title, text, url, and metadata (citation, version, and how to cite — including that author-draft page numbers must never be presented as journal pin cites; see metadata.citation_note). Very long documents (casebooks) are truncated; metadata.truncated is true and fetch_document(document_id, start_chunk=...) pages through the remainder. Args: id: The document id from a search result

  mcp-tool

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  {
    "type": "object",
    "title": "fetchArguments",
    "required": [
      "id"
    ],
    "properties": {
      "id": {
        "type": "string",
        "title": "Id"
      }
    }
  }

  arguments 13 lines copy
• fetch_document unknown never probed

  Retrieve the text of a specific Engelberg Center publication by document ID. Use after a search() call to get more context around a relevant chunk, or to read a paper in page order. Long documents are paged: at most max_chunks chunks are returned per call, starting at start_chunk. The response includes total_chunks and next_start_chunk (null when you have reached the end) — pass next_start_chunk back to continue reading. Returns document metadata (including version: "published" or "author_draft" — see citation_note for how to cite drafts) and text chunks in sequence with page numbers and section headings. Args: document_id: The document ID returned by search() or list_documents() start_chunk: Zero-based chunk offset to start from (default 0) max_chunks: Maximum chunks to return (default 40, max 100)

  mcp-tool

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  {
    "type": "object",
    "title": "fetch_documentArguments",
    "required": [
      "document_id"
    ],
    "properties": {
      "max_chunks": {
        "type": "integer",
        "title": "Max Chunks",
        "default": 40
      },
      "document_id": {
        "type": "string",
        "title": "Document Id"
      },
      "start_chunk": {
        "type": "integer",
        "title": "Start Chunk",
        "default": 0
      }
    }
  }

  arguments 23 lines copy

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