Semantic Scholar

@semanticscholar find papers by Geoffrey Hinton published in the last 3 years on deep learning architectures

Found 12 papers by Geoffrey Hinton (author ID: 1741101) from 2021-2024 matching 'deep learning architectures'. Top results include: 'Forward-Forward Algorithm' (2022, 847 citations), 'Distilling the Knowledge in a Neural Network' (2021, 1,203 citations), and 'A Simple Framework for Contrastive Learning' (2023, 412 citations). Each entry includes paper ID, title, publication year, citation count, and abstract excerpt. This represents a filtered subset of Hinton's recent work—actual results depend on Semantic Scholar's indexing coverage at query time.

This example shows the MCP's author-paper search capability, useful for literature reviews or tracking a researcher's recent contributions. Requires an API key with standard rate limits (100 requests/5 minutes for free tier). Results reflect Semantic Scholar's corpus, which may lag recent preprints by days or weeks. Citation counts update periodically, not in real-time.

@semanticscholar get the full citation graph for paper ID 649def34f8be52c8b66281af98ae884c09aef38b—show me what it references and what cites it

Paper 649def34f8be52c8b66281af98ae884c09aef38b ('Attention Is All You Need', Vaswani et al., 2017) references 43 papers and is cited by 89,247 papers in Semantic Scholar's database. Top references include 'Neural Machine Translation by Jointly Learning to Align and Translate' (Bahdanau, 2014) and 'Long Short-Term Memory' (Hochreiter, 1997). Top citing papers span transformer applications in NLP, vision, and biology. This illustrative example shows bidirectional citation traversal—actual counts and lists vary as the corpus grows.

Demonstrates the MCP's citation network tools, critical for understanding a paper's scholarly context and impact. Fetching both references and citations requires two API calls (one per direction). Large citation lists (like this example's 89k+ citers) return paginated—default is 100 results per call. Useful for mapping research lineage or finding derivative work.

@semanticscholar compare the citation trajectories of these three papers on reinforcement learning and summarize which approach gained more traction in the last 5 years

Analyzing papers 'Proximal Policy Optimization' (PPO, 2017), 'Soft Actor-Critic' (SAC, 2018), and 'Decision Transformer' (2021): PPO accumulated 8,200 citations with steady growth (avg 1,640/year). SAC shows 4,100 citations with acceleration post-2020 (avg 1,025/year). Decision Transformer has 890 citations despite being newer (avg 445/year since 2021). Citation velocity suggests PPO remains the dominant baseline, but SAC's recent uptick indicates growing adoption in continuous control tasks. Decision Transformer's trajectory is promising but too early to assess long-term impact. This synthesis pairs Semantic Scholar's citation data with the AI's comparative reasoning.

This example highlights how the MCP enables AI-driven research synthesis by combining citation metrics across multiple papers. Requires batch paper lookup (one API call for 3 papers) plus individual citation detail calls. The AI interprets trends—raw data doesn't include 'traction' analysis. Best for comparative literature reviews where quantitative citation patterns inform qualitative conclusions about field evolution.

When this MCP speeds up academic research synthesis

A 3-person research lab writing an NIH grant needs to cite 40-60 papers and trace citation chains to show their work builds on established findings. The Semantic Scholar MCP wins here because it surfaces paper metadata, author lists, and bidirectional citations (who cited this, what it references) without leaving the workspace. The team can query by paper ID or author ID, batch-fetch details for 20 papers at once, and cross-check citation counts to prioritize high-impact sources. The API key requirement is trivial (free tier covers most grant-writing volumes). If your lit review spans non-academic sources or needs full-text access, this MCP won't help—it's metadata and graph traversal only. For teams writing 2-4 grants a year, the time saved on citation hygiene alone justifies the setup.

Why this MCP falls short for patent landscape analysis

A 6-person biotech startup tracking competitor publications in CRISPR gene editing wants to monitor who's publishing what and where the citation momentum is shifting. Semantic Scholar's author and paper detail tools can surface recent papers and trace citation networks, but the MCP lacks filtering by publication date ranges, journal impact factors, or patent cross-references. You can batch-query papers and authors, but you're still manually triaging results in the workspace. If your competitive intel process runs weekly and hinges on spotting emerging authors or citation spikes, the 14-tool surface area here is overkill without the filtering primitives you'd get from a dedicated research intelligence platform. Use this MCP when you need one-off deep-dives on specific authors or papers, not for ongoing surveillance workflows.

When this MCP streamlines reviewer assignment decisions

A 2-person editorial team at a mid-tier CS journal assigns 15-20 papers to reviewers each month and needs to verify author expertise and check for citation conflicts. The Semantic Scholar MCP excels here: query an author by ID to see their publication history, pull their top-cited papers, and cross-check whether a proposed reviewer has cited (or been cited by) the submission's authors. The batch endpoints let you process multiple authors in one call, cutting the conflict-check phase from 30 minutes to under 5. The API key is free for editorial use-cases under 5,000 requests per month. If your journal publishes over 100 papers monthly or needs integration with manuscript management systems, you'll outgrow this MCP's manual query model. For small-to-mid-volume journals, it's the fastest way to make reviewer assignments defensible.