语义学者工具

Configuration File: semantic_scholar_tools.json Tool Type: Local Tools Count: 2

此页面包含在 semantic_scholar_tools.json 配置文件中定义的所有工具。

可用工具

SemanticScholar_get_pdf_snippets (Type: SemanticScholarPDFSnippetsTool)

Fetch a paper’s open-access PDF from Semantic Scholar and return bounded text snippets around pro…

SemanticScholar_get_pdf_snippets tool specification

工具信息：

  • Name: SemanticScholar_get_pdf_snippets

  • Type: SemanticScholarPDFSnippetsTool

  • Description: Fetch a paper’s open-access PDF from Semantic Scholar and return bounded text snippets around provided terms. Uses markitdown to convert PDF to markdown. Useful when key details (methods, experimental parameters) are in the full text rather than abstract.

参数：

  • paper_id (string) (optional) Semantic Scholar paper ID (e.g., from SemanticScholar_search_papers). The tool will fetch the PDF URL if available.

  • open_access_pdf_url (string) (optional) Direct open access PDF URL (recommended when you already have it from SemanticScholar_search_papers).

  • terms (array) (required) Terms to search for in the extracted full text (case-insensitive).

  • window_chars (integer) (optional) Context window size (characters) before and after each match.

  • max_snippets_per_term (integer) (optional) Maximum number of snippets returned per term.

  • max_total_chars (integer) (optional) Hard cap on total characters returned across all snippets.

示例用法：

query = {
    "name": "SemanticScholar_get_pdf_snippets",
    "arguments": {
        "terms": ["item1", "item2"]
    }
}
result = tu.run(query)

**SemanticScholar_search_papers**（类型：SemanticScholar工具）

Search for papers on Semantic Scholar including abstracts and AI-generated TLDR summaries. This t…

SemanticScholar_search_papers 工具规范

工具信息：

  • 名称：SemanticScholar_search_papers

  • 类型：SemanticScholarTool

  • Description: Search for papers on Semantic Scholar including abstracts and AI-generated TLDR summaries. This tool queries the Semantic Scholar API using natural language keywords and returns papers with details such as title, abstract, TLDR summary, fields of study, publication year, journal (venue), and URL. Rate limits: 1 req/sec without API key, 100 req/sec with SEMANTIC_SCHOLAR_API_KEY environment variable set.

参数：

  • ``query``（字符串）（必填）用于Semantic Scholar的搜索查询。使用以空格分隔的关键词来优化搜索结果。

  • limit (integer) (optional) Maximum number of papers to return from Semantic Scholar.

  • year (string) (optional) Filter results by publication year. Use a single year (e.g., ‘2024’) or a range (e.g., ‘2020-2024’).

  • sort (string) (optional) Sort results. Options: ‘citationCount:desc’, ‘citationCount:asc’, ‘publicationDate:desc’, ‘publicationDate:asc’.

  • include_abstract (boolean) (optional) If true, best-effort fetches missing abstracts via the paper detail endpoint (only when abstract is missing in search results).

示例用法：

query = {
    "name": "SemanticScholar_search_papers",
    "arguments": {
        "query": "example_value"
    }
}
result = tu.run(query)