Academic Search MCP
Allows searching academic literature through the Semantic Scholar API, with tools for relevance search, batch metadata retrieval, and citation-graph recommendations.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Academic Search MCPsearch for papers on transformer models from 2020 with at least 100 citations"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Academic Search MCP Server
A Model Context Protocol (MCP) server that lets Claude Code (or any MCP client) search academic literature through the Semantic Scholar API. It is built for research workflows: relevance search with filters, batch metadata retrieval, and citation-graph recommendations.
This is a modified fork of afrise/academic-search-mcp-server.
The server was rewritten on top of the official semanticscholar
Python library and extended with batch and recommendation tools, bounded retry on rate limits, and
clearer error handling. See Changes from upstream below.
Tools
Tool | What it does |
| Relevance search with optional filters: |
| Same as |
| Full metadata for a single paper by Semantic Scholar ID, DOI, or arXiv ID. |
| Details for up to 500 papers in one request — far cheaper than looping |
| Citation-graph recommendations from seed paper IDs ( |
Each tool returns a formatted text block per paper: ID, title, authors, year, DOI, venue, citation count, fields of study, open-access status, PDF URL, abstract, and TL;DR when available.
Related MCP server: Semantic Scholar MCP Server
Requirements
Python 3.10+
uv (recommended) — or plain
pipA Semantic Scholar API key is optional (see below)
Install
git clone https://github.com/ociupitu/academic-search-mcp.git
cd academic-search-mcp
uv sync # creates .venv and installs dependencies from uv.lockPrefer pip? pip install -e . inside a virtual environment works too.
API key (optional)
The server reads SEMANTIC_SCHOLAR_API_KEY from the environment, but it is optional:
Without a key — requests go through Semantic Scholar's shared anonymous pool. It works, but you are more likely to hit
HTTP 429rate limiting during busy periods.With a free key — you get your own quota. Register at https://www.semanticscholar.org/product/api and set the variable (the
.mcp.jsonexample below wires it in).
Either way the server keeps requests sequential and does a short bounded retry on a 429, then
returns a readable Error: string rather than hanging or silently returning "no results".
Use with Claude Code
Add the server to your client's MCP config (for Claude Code, a .mcp.json in your project root).
Point --directory at wherever you cloned this repo, and use an absolute path:
{
"mcpServers": {
"academic-search": {
"type": "stdio",
"command": "uv",
"args": ["run", "--directory", "/absolute/path/to/academic-search-mcp", "server.py"],
"env": {
"SEMANTIC_SCHOLAR_API_KEY": "YOUR_API_KEY_HERE"
}
}
}
}Leave the env block out entirely to run keyless. Restart your MCP client after editing the config.
Changes from upstream
Rewritten on the official
semanticscholarlibrary instead of hand-rolledhttpxcalls.Added
get_papers_batch(batch details) andrecommend_papers(citation-graph recommendations).Added search filters:
fields_of_study,min_citation_count,venue,open_access_pdf, and a flexibleyearrange.Fail-fast client (
retry=False) plus a bounded in-server retry on 429, so a throttle recovers in seconds or returns a clean error instead of blocking for minutes.Dropped the Crossref path; Semantic Scholar is the single source.
License
AGPL-3.0, inherited from the upstream project. See LICENSE. If you redistribute or run a modified version as a network service, the AGPL's source-availability terms apply.
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