{"uid":"cap_7o4xO5D8M4YHPHPgNVUTN","slug":"company-enrich-agentdiet-dev-arxiv-ai-cs-paper-search-structured-digest-ea48ac4e","name":"ArXiv AI/CS Paper Search & Structured Digest","description":"Queries ArXiv for AI/CS/math papers and returns structured abstracts, authors, PDF links, and BibTeX. (Live x402 v2, IETF MPP & MCP endpoint at https://company-enrich.agentdiet.dev/v1/llm-benchmark-papers)","url":"https://company-enrich.agentdiet.dev/v1/llm-benchmark-papers?utm_source=zero.xyz","method":"GET","headers":{},"bodySchema":{"type":"object","required":["query"],"properties":{"query":{"type":"string","minLength":1,"description":"ArXiv paper ID (e.g. 2308.11432) or research topic search query"}}},"responseSchema":{"type":"object"},"example":null,"exampleRequest":null,"tags":[],"displayCostAmount":"0.01","displayCostAsset":"USD","priceDynamic":false,"priceHint":null,"priceStatus":"priced","priceSource":"probe","requiresHandshake":false,"reviewCount":0,"rating":{"score":"0.00","successRate":"0.00","reviews":0,"stars":null,"state":"unrated"},"availabilityStatus":"unknown","priceObserved":null,"sessionDeposit":null,"pricing":{"kind":"static","summary":"$0.01/call","primary":{"kind":"static","protocol":"mpp","network":"tempo","amountUsd":"0.01","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"mpp","network":"tempo","amountUsd":"0.01","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_u-4EE4xgiRr49TeGgWOQu","protocol":"mpp","methodType":"crypto","chain":"tempo","mode":"charge","costAmount":"0.01","costPer":"request","priority":0,"asset":"0x833589fcd6edb6e08f4c7c32d4f71b54bda02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":"company-enrich.agentdiet.dev","brandSlug":"company-enrich-agentdiet-dev-mpp","brandBaseUrl":"https://company-enrich.agentdiet.dev","brandDocsUrl":null,"whatItDoes":"Searches ArXiv for AI, CS, and math papers by topic or paper ID and returns structured abstracts, author lists, PDF links, and BibTeX citations.","exampleAgentPrompt":"Can you find me the abstract, authors, PDF link, and BibTeX for the ArXiv paper on GPT-4 evaluation benchmarks — search for 'GPT-4 benchmark evaluation' and give me the structured details?","exampleUseCases":[{"title":"Literature review on a research topic","prompt":"I'm writing a paper on retrieval-augmented generation — can you search ArXiv for recent papers on that topic and give me the titles, abstracts, authors, and BibTeX entries so I can build my references?"},{"title":"Fetch a specific paper by ArXiv ID","prompt":"Look up ArXiv paper 2305.10601 and give me the full structured metadata — abstract, authors, PDF link, and BibTeX citation."},{"title":"Competitive benchmarking research","prompt":"Search ArXiv for papers about LLM benchmark datasets from the last year and pull back the abstracts and author lists so I can see what benchmarks are being proposed."}],"resultDescription":"Returns a structured object containing paper title, abstract text, author names, ArXiv paper ID, direct PDF URL, publication date, and a formatted BibTeX citation entry for one or more matched papers.","failureModes":["No papers found for the given query — returns empty results or an error indicating no matches","Invalid ArXiv paper ID format causes a lookup failure","ArXiv API rate limiting or downtime causes a service error","Query too broad returns irrelevant or low-quality paper matches","Payment not completed (HTTP 402) if x402 micropayment is not handled"],"whenToPreferThis":"Choose this endpoint when you need structured, machine-readable academic paper metadata from ArXiv including BibTeX — especially for AI, CS, and math domains. Prefer this over raw ArXiv HTML scraping when you need clean, token-efficient abstracts and ready-to-use citations. It is well-suited for agents building literature reviews, citation databases, or research digests without parsing ArXiv XML feeds directly.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-21T07:23:35.053Z","isFirstParty":false,"canonicalSlug":"company-enrich-agentdiet-dev-arxiv-ai-cs-paper-search-structured-digest-c14e4310"}