paper-details

---

name: paper-details description: Retrieve comprehensive details about a specific academic paper including abstract, citations, references, and related work. Use when you have a paper ID (DOI, ArXiv, etc.) and need full metadata, citation information, or want to explore a paper's connections. allowed-tools: Bash, Read, Grep, Glob, TodoWrite

<quick_start> To get paper details:

Copy/asta:paper-details arxiv:1706.03762

With optional expansions:

Copy/asta:paper-details "10.1038/nature14539" --include citations,references

</quick_start>

<success_criteria>

• Successfully resolves paper ID to full metadata
• Returns abstract and TLDR (AI-generated summary)
• Provides citation count and key metrics
• Includes links to full text when available
• Optionally expands to show citations, references, or author's other work </success_criteria>

Available Fields: abstract, authors, citations, citationCount, fieldsOfStudy, influentialCitationCount, isOpenAccess, journal, publicationDate, references, tldr, url, venue, year

1. Parse the input to determine ID type:

   • DOI pattern: 10.xxxx/xxxxx
   • ArXiv pattern: arxiv:XXXX.XXXXX or XXXX.XXXXX
   • Semantic Scholar: CorpusId:XXXXXXX
   • Title: Quoted string without ID pattern

2. If title provided, use mcp__asta__snippet_search to find the paper first

Phase 2: Retrieve Details

1. Use mcp__asta__get_paper with:

   • paper_id: The resolved paper ID
   • fields: Request all relevant fields

2. Parse and format the response

Phase 3: Expand (if requested)

1. If --include citations: Use mcp__asta__get_paper_citations
2. If --include references: Use mcp__asta__get_paper_references
3. If --include authors: Use mcp__asta__get_author_papers for each author

<output_format> Structure the output as:

Copy# {Paper Title}

## Quick Summary
**TLDR:** {AI-generated summary from Asta}

## Metadata

| Field | Value |
|-------|-------|
| Authors | {Full author list with affiliations} |
| Year | {Year} |
| Venue | {Conference or Journal} |
| Citations | {count} ({influential_count} influential) |
| Open Access | {Yes/No} |
| Fields | {fieldsOfStudy} |

## Abstract
{Full abstract text}

## Links
- **DOI:** [{doi}](https://doi.org/{doi})
- **ArXiv:** [{arxiv_id}](https://arxiv.org/abs/{arxiv_id})
- **PDF:** {pdf_link if available}
- **Semantic Scholar:** [View on S2]({s2_url})

## Key Citations (if requested)
{Top 5 most influential papers that cite this one}

| Paper | Year | Citations |
|-------|------|-----------|
| {Title} | {Year} | {count} |

## Key References (if requested)
{Most cited references from this paper}

## Related Work by Authors (if requested)
{Other notable papers by the same authors}

</output_format>

Quick Summary

TLDR: A new neural network architecture based solely on attention mechanisms, dispensing with recurrence and convolutions entirely, achieving state-of-the-art results on machine translation.

Metadata

Abstract

The dominant sequence transduction models are based on complex recurrent or convolutional neural networks that include an encoder and a decoder. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely...

Links

• ArXiv: 1706.03762
• PDF: Download
• Semantic Scholar: View on S2

<anti_patterns>

• Don't guess IDs - If ID format is unclear, ask for clarification
• Don't fabricate metadata - Only return data from the API
• Don't skip the TLDR - Include the AI-generated summary when available
• Don't return partial data - If a field is unavailable, note it explicitly </anti_patterns>

<common_papers> For testing or quick reference:

• Attention paper: arxiv:1706.03762
• BERT: arxiv:1810.04805
• GPT-3: arxiv:2005.14165
• AlphaFold: DOI:10.1038/s41586-021-03819-2
• ResNet: arxiv:1512.03385 </common_papers>