# Stanford Semantic Scholar MCP for AI Agents AI Agent Connect > Stanford Semantic Scholar MCP lets your AI client search over 200 million academic papers. It finds citations, pulls author metrics like h-indexes, and suggests related research based on specific seed papers. It's built for anyone who needs to navigate the academic graph without manually digging through search results. ## Overview - **Category:** education - **Price:** Free - **Endpoint:** https://edge.vinkius.com/vk_preview_AZJpPbxBFEPF3qqoPfdyST9yhfXQRp6VrfLZlw40/ai-agent-connect - **Tags:** semantic-scholar, academic-papers, citations, research, literature-review, bibliography, ai-recommendations ## Description Researching a new field usually means spending hours jumping between tabs, copy-pasting abstracts, and trying to figure out who actually cited whom. This Connector changes that by giving your agent a direct line to the Semantic Scholar Academic Graph. Instead of you doing the heavy lifting, you can ask your agent to map out a literature review or find the specific papers that influenced a certain discovery. It handles the tedious parts like pulling h-indexes for a list of authors or finding every paper a specific journal published in a year. You'll find it's a lot faster to build a bibliography when your agent can handle batch requests and fuzzy title matching to get you exactly what you need. Because Vinkius hosts this Connector, you can plug it into your existing workflow and start querying the world's largest free academic knowledge graph immediately. It turns a manual search process into a conversational research assistant that actually understands the context of your queries. ## Tools ### batch_get_authors Pull multiple author profiles in one request for easier collaboration analysis. This saves time when comparing several researchers. ### get_author_papers List all publications by a specific author including venue and year. Use this to review a researcher's body of work. ### get_paper Pull full metadata, abstracts, and PDF URLs using DOIs, ArXiv IDs, or PubMed IDs. It handles multiple ID formats. ### search_authors Search the academic graph to find researchers by name and see their metrics. Use this to find top contributors in a field. ### batch_get_papers Fetch multiple papers at once using various ID formats like DOI or ArXiv ID. This is perfect for building reading lists quickly. ### bulk_search_papers Retrieve large sets of papers using continuation tokens for systematic reviews. This is ideal for handling large result sets. ### get_author Get a researcher's h-index, citation count, and affiliation details. It provides a clear view of academic impact. ### get_multi_recommendations Find papers similar to a positive set but different from a negative set. This helps with focused literature discovery. ### get_paper_authors Identify the authors of a specific paper along with their individual h-indexes. This is useful for mapping collaboration networks. ### get_paper_citations Find every paper that has cited a specific work to track its impact. It returns titles, venues, and years. ### get_paper_references See the foundational papers a specific work referenced during its development. This helps trace the intellectual lineage of a study. ### get_recommendations Get AI-driven suggestions for papers similar to a single seed paper. It analyzes citation patterns and content similarity. ### match_paper_title Use fuzzy matching to find the correct paper metadata from a slightly messy title string. This resolves issues with old reference lists. ### search_by_field Filter paper searches by specific fields like Medicine, Physics, or Computer Science. It covers over 20 different fields. ### search_by_venue Narrow your search to specific journals like Nature or conferences like NeurIPS. This tracks publications in top-tier venues. ### search_papers Search 200M+ papers by keyword with filters for year, venue, and open access. It provides a broad overview of academic content. ## Prompt Examples **Prompt:** ``` Find the most cited papers on transformer architectures published since 2020. ``` **Response:** ``` I've searched the academic graph for papers on "transformer architecture" published between 2020 and 2026. Here are the top results sorted by citation count: | Paper Title | Venue | Citations | | :--- | :--- | :--- | | **Attention Is All You Need** | N/A | 100,000+ | | **Vision Transformer (ViT)** | CVPR | 15,000+ | | **BERT: Pre-training of Deep Bidirectional Transformers** | NAVL | 120,000+ | These works form the foundation of modern transformer research. ``` **Prompt:** ``` What is Geoffrey Hinton's h-index and how many papers has he published? ``` **Response:** ``` I've pulled the metrics for Geoffrey Hinton from the academic graph. Here is his current profile: * **Total Papers:** 400+ * **Citation Count:** 500,000+ * **h-index:** 100+ * **Affiliations:** University of Toronto, Google He remains one of the most highly cited and influential figures in computer science. ``` **Prompt:** ``` Recommend papers similar to "Attention Is All You Need". ``` **Response:** ``` Based on the citation patterns and content similarity to "Attention Is All You Need", here are some highly relevant papers you should check out: * **BERT** (Pre-training of Deep Bidirectional Transformers) * **GPT-2** (Language Modeling is Superhuman) * **Universal Transformer** * **Transformer-XL** These works built directly upon the attention mechanism paradigm established in your seed paper. ``` ## Capabilities ### Search 200M+ papers Find specific research using filters for year, field of study, and venue. ### Map citation chains See who cited a paper and what papers a specific work referenced. ### Pull author metrics Get h-indexes, citation counts, and affiliations for any researcher. ### Get research recommendations Use AI to find papers similar to your current seed papers. ### Process batch requests Retrieve multiple papers or author profiles in a single operation. ### Fuzzy match titles Find the correct paper metadata even when the title string is slightly off. ## Use Cases ### Literature Review A PhD student asks for papers on transformer architectures from 2020 to 2024, and the agent pulls a sorted list using search_papers. ### Impact Analysis A data scientist wants to see how many times a specific Nature paper has been cited in the last 5 years using get_paper_citations. ### Author Scouting An R&D lead wants to find the top 10 authors in Quantum Computing and see their primary affiliations using search_authors. ### Bibliography Cleanup A researcher has a list of 50 messy titles and needs the DOIs and abstracts for all of them using match_paper_title. ## Benefits - Skip the manual search by using search_papers to find specific research across 200M+ records instantly. - Map out research lineages quickly using get_paper_citations and get_paper_references to see how ideas evolve. - Build accurate bibliographies faster by using match_paper_title to resolve messy titles from old reference lists. - Analyze researcher impact in seconds with get_author to see h-indexes and total citation counts. - Discover new reading lists using get_recommendations which uses AI to find papers similar to your current interests. - Handle massive literature reviews with bulk_search_papers which supports pagination for large datasets. ## How It Works The bottom line is you get a direct, conversational interface to the world's largest academic knowledge graph. 1. Subscribe to the Stanford Semantic Scholar MCP on Vinkius. 2. Connect your preferred AI client to the Connector. 3. Ask your agent to find papers, authors, or citations. ## Frequently Asked Questions **Can Stanford Semantic Scholar MCP find papers from specific journals?** Yes, it can filter results by specific venues like Nature, Science, or top-tier conferences like NeurIPS and CVPR. **Does the Stanford Semantic Scholar MCP require an API key?** No, this Connector uses a public API, so you can start searching immediately without setting up your own credentials. **Can I use Stanford Semantic Scholar MCP to find author h-indexes?** Yes, it pulls core metrics including h-index, total citations, and paper counts for researchers across the academic graph. **How does Stanford Semantic Scholar MCP handle large searches?** It supports bulk search operations with continuation tokens, which is perfect for systematic reviews where you need to see more than just the top few results. **Can Stanford Semantic Scholar MCP recommend new research?** Yes, it uses an AI-powered recommendation engine to suggest papers that are similar to your seed papers based on citation networks. **Does Stanford Semantic Scholar MCP support DOIs and ArXiv IDs?** Yes, you can retrieve full paper details using DOIs, ArXiv IDs, PubMed IDs, or Semantic Scholar IDs. **Do I need an API key?** No. The Semantic Scholar API is fully public. An optional free API key increases rate limits from 1 to 10 requests per second. **What paper ID formats are supported?** Semantic Scholar accepts multiple ID formats: its own S2 Paper ID, DOI (e.g. "10.1038/..."), ArXiv ID (e.g. "arXiv:2106.09685"), PubMed ID (e.g. "PMID:34845388"), and ACL Anthology ID. This makes it easy to look up any paper regardless of where you found the reference. **How do the AI recommendations work?** The recommendation engine uses machine learning to analyze citation patterns, co-citation networks, and content similarity. You can provide one seed paper for basic recommendations, or multiple positive and negative seed papers for advanced filtering. This is the most sophisticated way to discover related literature.