Academia MCP Server

Academia mcp mcp server lets Claude, Cursor and other MCP clients work with Academia MCP directly. MCP server with tools to search, fetch, analyze, and report on scientific papers and datasets.

What Academia MCP does

MCP server with tools to search, fetch, analyze, and report on scientific papers and datasets.

Key capabilities

• ArXiv search and download
• ACL Anthology search
• Hugging Face datasets search
• Semantic Scholar citations and references
• Web search via Exa, Brave, or Tavily
• Web page crawler, LaTeX compilation, PDF reading
• Optional LLM-powered tools for document QA and research proposal workflows

Tools it exposes

Once connected, the assistant can call these 14 tools directly:

• arxiv_search — Query arXiv with field-specific queries and filters
• arxiv_download — Fetch a paper by ID and convert to structured text (HTML/PDF modes)
• anthology_search — Search ACL Anthology with fielded queries and optional date filtering
• hf_datasets_search — Find Hugging Face datasets with filters and sorting
• s2_get_citations — List papers citing a given arXiv paper (Semantic Scholar Graph)
• s2_get_references — List papers referenced by a given arXiv paper
• visit_webpage — Fetch and normalize a web page
• web_search — Unified search wrapper; available when at least one of Exa/Brave/Tavily keys is set
• compile_latex — Compile LaTeX to PDF in WORKSPACE_DIR
• read_pdf — Extract text per page from a PDF
• document_qa — Answer questions over provided document chunks (requires LLM)
• Features — The Features tool exposed by this server
• Requirements — The Requirements tool exposed by this server
• Quickstart — The Quickstart tool exposed by this server

Installing the academia mcp mcp server

Setup follows the standard MCP pattern: clone or install the server, then register it in your client's configuration file and restart the client. The configuration snippets on this page cover Claude Desktop, Claude Code and Cursor.

Configuration

Before the server will start you need to supply 7 environment variables: ENABLE_AUTH, TOKENS_FILE, OPENROUTER_API_KEY, EXA_API_KEY, BRAVE_API_KEY, TAVILY_API_KEY, BASE_URL. Keep credentials in your client's env block or a secrets manager rather than committing them.

Requirements

• Python 3.12+

Where it fits

AI-service servers chain other models into your assistant, turning a single chat into a small production pipeline. Academia MCP sits in that group, and the shape of its toolset — arxiv_search, arxiv_download, anthology_search among others — tells you what it is really for. Worth comparing against the other ai services servers in this directory before you commit to one, since several overlap in scope but differ sharply in setup cost and permissions.

Practical notes

• This server runs locally, so it operates with whatever access your machine and its credentials already have. Scope that deliberately rather than by default.
• It will not start until its required credentials are present, so set those before wondering why the tools never appear.
• With 14 tools exposed, expect a noticeable bump in prompt size — disable it in projects that never use Academia MCP.
• Maintained by IlyaGusev, written in Python.
• MCP clients ask for confirmation before each tool call by default. Keep that on while you learn what the academia mcp mcp server actually does with your data.
• Every entry in this directory is reviewed by hand before it goes live, and details are checked against the project's own documentation.