Semantic Scholar MCP. Analyze research impact and trace academic citations.
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Semantic Scholar connects your AI agent to a massive academic knowledge graph containing 200M+ papers. It summarizes research articles with AI-generated TLDRs and tracks influence beyond simple citation counts.
You can find specific authors' full metrics, trace citation chains, or look up any paper using its DOI, ArXiv ID, or PubMed ID.
What your AI agents can do
Get semantic citations
Finds other papers that specifically cite a given piece of work, essential for tracing research impact.
Get semantic paper
Retrieves complete details about one paper using its DOI, ArXiv ID, PMID, or Semantic Scholar ID.
Search semantic author
Returns a researcher's publication count, total citations, and h-index score on Semantic Scholar.
Runs a broad search across 200M+ scholarly works, returning results with AI summaries, citation counts, and fields of study.
Pulls all available data for one specific paper using its unique identifier (Semantic Scholar ID, DOI, ArXiv ID, or PMID).
Identifies and retrieves papers that specifically cite a given source, helping trace how research ideas spread.
Looks up an academic's profile to get their total paper count, lifetime citation count, and h-index score.
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Supported MCP Clients
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Semantic Scholar: 4 Tools for Deep Research Analysis
Use these four tools to analyze academic citations, retrieve paper details, and measure researcher influence across all STEM fields.
019d7605get semantic citations
Finds other papers that specifically cite a given piece of work, essential for tracing research impact.
019d7605get semantic paper
Retrieves complete details about one paper using its DOI, ArXiv ID, PMID, or Semantic Scholar ID.
019d7605search semantic author
Returns a researcher's publication count, total citations, and h-index score on Semantic Scholar.
019d7605search semantic scholar
Searches 200M+ papers across STEM fields, providing AI summaries, citation counts, and influence scores.
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Build Your Own
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What you can do with this MCP connector
Listen up. This ain't your grandpa's library database. You're connecting your AI agent straight into Semantic Scholar, a massive knowledge graph containing over 200 million academic papers across every STEM field—from medicine and biology to computer science. When you use this server, you’re not just getting search results; you're getting structured access to how research actually moves.
When you need to dive deep into the scholarly world, you start with search_semantic_scholar. You run a broad query across that huge corpus of works. The result isn't just a list of titles; your agent gets an AI-generated TLDR for each paper, distilling the core insight down to one sentence.
Beyond the summary and basic citation count, you get influence scores—a metric that tells you how meaningful a paper is in the conversation. You can search across fields knowing exactly what kind of data set you're pulling from.
Need details on just one piece? get_semantic_paper handles it. You don't have to worry about which ID to use; it pulls all the metadata for a single paper whether you give it the DOI, an ArXiv ID, a PubMed ID (PMID), or the Semantic Scholar ID itself. It gives you every piece of data point available for that specific source.
But finding a paper is half the battle. Understanding its impact is what matters. You use get_semantic_citations to trace how research ideas actually spread. Instead of just seeing who cited it, this tool finds other papers that specifically build on or reference the original work. This lets you map out the entire lineage of an idea—you can follow a key finding and see exactly which subsequent works built upon it.
When you combine these tools, your agent builds powerful citation graphs. You'll get those TLDR summaries for every single citing paper as well, so you don't just know that a concept was referenced; you know how the referencing work talks about it. It’s all connected.
And if you gotta track down the people doing the actual groundbreaking work, search_semantic_author is what you need. You plug in an academic's name and you get their full profile metrics: their total number of publications, their lifetime citation count, and that crucial h-index score. This gives you a clean measure of their overall impact on the field.
Bottom line? You use this server to make your AI agent act like a research librarian who's also a grad student—super smart, super fast, and knows exactly where all the information is buried.
How Semantic Scholar MCP Works
- 1 Start by calling
search_semantic_scholarwith a general topic query. This gives your agent an initial list of relevant papers and their summaries. - 2 Next, use the IDs from that result to call
get_semantic_citations. This narrows the focus by showing which specific works built upon the original paper. - 3 Finally, if you need more context, pass a single Paper ID into
get_semantic_paperfor full metadata retrieval.
The bottom line is that your agent systematically moves from general topic search to targeted impact analysis by chaining these specialized tools together.
Who Is Semantic Scholar MCP For?
Graduate students and R&D teams. If you're drowning in literature reviews, this server pulls the threads out for you. It helps you prove where an idea came from, who influenced it, and which researchers are actually leading the field.
Needs to build a comprehensive literature review. They use get_semantic_citations repeatedly to map out how a core idea evolved over decades.
Requires quick assessment of paper relevance before reading the full text. They use the main search function to get TLDR summaries and check influence scores.
Needs to evaluate a candidate's true research impact, not just their publication count. They run search_semantic_author to analyze h-index and total citations.
What Changes When You Connect
- Track genuine research lineage. Instead of just seeing a citation count,
get_semantic_citationsshows you which papers meaningfully build on the original work, giving you true influence metrics. - Rapidly assess paper relevance. Running
search_semantic_scholarreturns AI-generated TLDR summaries for 200M+ works, letting your agent filter out noise before you read anything. - Validate researcher impact immediately. Use
search_semantic_authorto pull a candidate's h-index and total citation metrics instantly, which is better than just looking at their CV page. - Consolidate data lookups. You don't need four different APIs; the server accepts DOI, ArXiv ID, or PMID for any paper lookup using
get_semantic_paper. - Build deep knowledge graphs. The combination of searching with
search_semantic_scholarand then runningget_semantic_citationsallows your agent to map out an idea's entire academic history.
Real-World Use Cases
Identifying the missing piece in a literature review
A PhD student writes about quantum computing but realizes their core theory (Paper A) might be under-cited. They ask their agent to run get_semantic_citations on Paper A. The server returns ten niche papers that cite it, revealing an entire subfield they hadn't considered.
Vetting a potential collaborator
An R&D team needs to hire an expert in CNNs. Instead of reading the candidate’s vague LinkedIn profile, they run search_semantic_author on the candidate's name. The server immediately returns their h-index (162) and 380k+ citations, confirming massive impact.
Comparing multiple theories quickly
A researcher needs to compare LoRA vs. traditional fine-tuning. They use search_semantic_scholar with the query 'low-rank adaptation large language models'. The agent returns several key papers, each with a TLDR summary comparing parameter reduction and memory savings.
Deep diving into an obscure topic
A student finds one paper (Paper B) on an old algorithm. To understand its context, they first use get_semantic_paper to get the full metadata of Paper B. Then, they run get_semantic_citations on it to see every major work that built upon this foundational research.
The Tradeoffs
Treating it like a general search engine
Asking the agent, 'What's the best way to learn Python?' and expecting academic papers. The server is for deep scientific data only.
→
Use search_semantic_scholar with specific technical terms (e.g., 'transformer architecture machine translation') to get relevant research articles instead of general guides.
Forgetting the ID requirements
Trying to run a citation check using just keywords like 'Attention is all you need'. The tools require a precise identifier, not loose text.
→
First, use search_semantic_scholar or get_semantic_paper to find the specific DOI or ArXiv ID for that paper. Then pass the required ID into get_semantic_citations.
Over-relying on citation counts alone
Assuming a high citation count means the work is currently relevant. The server provides 'influential citations' which track meaningful academic builds.
→
Always cross-reference total citations with the influential citation metric provided by search_semantic_scholar to gauge current, active impact.
When It Fits, When It Doesn't
Use this MCP Server if your goal is deep literary analysis or tracking scientific influence. Specifically, use it when you need to answer questions like: 'Who built on this work?' or 'What did this paper actually say in plain English?'. You must be working with academic IDs (DOI, ArXiv ID) or specific concepts within STEM fields.
Don't use this if you just need basic facts, general knowledge, or non-academic data. If your query is about current events or common cultural topics, the server won't help — that's a general search tool problem. Stick to tracing academic influence via get_semantic_citations and analyzing research scope with search_semantic_scholar. If you only have an author's name but no metrics, use search_semantic_author; otherwise, start the process by finding the paper itself.
Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Semantic Scholar. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.
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Works with Claude, ChatGPT, Cursor, and more
The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.
This server provides 4 capabilities that interface natively with Claude, ChatGPT, Cursor, and any MCP client. No middleware. No custom integration required.
Available Capabilities
Academic literature review shouldn't feel like a full-time job.
Today, doing a comprehensive literature review means clicking through Google Scholar results, manually pulling DOIs, cross-referencing citation counts in three different databases, and then spending hours summarizing what everyone else wrote. It’s exhausting and inefficient.
With this Semantic Scholar MCP Server, your agent does the heavy lifting. You feed it a topic or an initial paper ID, and it pulls not just citations, but structured data: TLDR summaries for every related work, all while keeping track of which ideas truly influenced others. The result is a map, not a list.
Semantic Scholar MCP Server: Analyze research impact instantly.
Manually tracking the evolution of an idea requires searching by keywords, then finding papers that cite those papers, and finally manually reading every abstract to see how the concepts built up. It's a multi-day process just for one chapter.
This server automates that chain reaction. You run `get_semantic_citations` on a foundational paper, and you get an immediate, structured list of all subsequent works, each with its own summary. The data flow is seamless: idea -> citation -> impact.
Common Questions About Semantic Scholar MCP
How do I use the `search_semantic_scholar` tool? +
You pass a broad topic or field of study to search_semantic_scholar. It returns hundreds of papers, each with an AI-generated summary and influence score, helping you narrow your focus fast.
What's the difference between citation count and influential citations? +
Influential citations are more valuable. While a standard citation just means someone mentioned the paper, an influential one means they built a meaningful new concept or method using the original work. search_semantic_scholar tracks this distinction.
Can I use `get_semantic_paper` if I only have a PubMed ID? +
Yes, get_semantic_paper accepts multiple identifiers, including the PMID. As long as you provide the correct format for the ID, it retrieves all the full metadata.
How does `search_semantic_author` help with hiring? +
search_semantic_author gives objective metrics (h-index, total citations) about an academic's career impact. This helps you assess their true contribution to a field over time.
What are the rate limits when running multiple queries using the `search_semantic_scholar` tool? +
The server manages throughput to maintain stability, so you generally won't hit hard rate limits for standard usage. If your agent executes large batches of searches rapidly, it might encounter temporary throttling; in that case, pacing the requests slightly helps.
If I use the `get_semantic_paper` tool with an invalid ID, what kind of error does my AI client receive? +
The tool returns a specific, machine-readable error payload if the provided identifier (DOI, ArXiv, etc.) is malformed or doesn't match any record. Your agent can reliably catch this standard failure response to adjust its search criteria.
Does using the `search_semantic_author` tool require me to provide API keys or credentials? +
No, you don't need to manage any credentials for basic author searches. The server handles all authentication internally once connected via Vinkius, making it ready to use right out of the box.
What academic fields does the `search_semantic_scholar` tool draw papers from? +
The search covers major STEM disciplines like Computer Science, Medicine, Biology, and Physics. It indexes over 200 million works, allowing you to find cross-disciplinary research across these core scientific areas.
Do I need an API key to use Semantic Scholar? +
No API key is required for basic usage. The public API provides 5,000 requests per 5 minutes shared among unauthenticated users. For higher throughput, academic and institutional users can request a free API key at semanticscholar.org, which grants dedicated rate limits of 1–10 requests per second depending on the endpoint.
What is the TLDR feature and how does it work? +
TLDR (Too Long; Didn't Read) is an AI-generated one-sentence summary of each paper, powered by Allen AI's SciTLDR NLP model. It distills the key contribution or finding of a paper into a single, easily digestible sentence — ideal for quickly scanning relevance without reading an entire abstract or paper.
What is the difference between total citations and influential citations? +
Total citations count every paper that references the work. Influential citations are a subset — papers where the cited work meaningfully contributes to the citing paper's research (not just a passing mention in the related work section). This metric is calculated by Semantic Scholar's AI models and provides a much more accurate measure of real scientific impact.
Use it with your favorite AI tools
Connect this server to Cursor, Claude, VS Code, and more.
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