A [Model Context Protocol (MCP)](https://www.anthropic.com/news/model-context-protocol) server that enables searching and retrieving academic paper
A Model Context Protocol (MCP) server that enables searching and retrieving academic paper information from multiple sources. That is what the academic search mcp server brings to an AI assistant: the same capability, reachable through the Model Context Protocol rather than a separate app or dashboard.
Configuration is passed through the environment: SEMANTIC_SCHOLAR_API_KEY, CROSSREF_API_KEY. Treat anything key-shaped as a real credential — scope it to the minimum the server needs, and rotate it if it ever lands in a shared config.
@smithery/cli on npm is all you need. Most clients run it directly, so configuration is a few lines and a restart.
This sits in the AI and media services group, where several servers overlap in what they claim to do but differ sharply once you actually set them up. It is maintained by afrise; worth a glance at recent repository activity before you build anything load-bearing on it.
We check each listing at SyncDev against the project's documentation before it goes live — if something here drifts out of date, it is a bug worth reporting.
{
"mcpServers": {
"academic-search": {
"command": "uv",
"args": ["run ", "/path/to/server/server.py"],
"env": {
"SEMANTIC_SCHOLAR_API_KEY": "your_key_here",
"CROSSREF_API_KEY": "your_key_here"
}
}
}
}Configuration as documented by the project. Restart the client after saving.
| Variable | Description | Required |
|---|---|---|
| SEMANTIC_SCHOLAR_API_KEY | Credential the server authenticates with. | Yes |
| CROSSREF_API_KEY | Credential the server authenticates with. | Yes |
Build a programmable telecommunications stack for connecting telephony services with the Internet via a cloud-based utility.
Search built for AI, not humans — semantic web search that returns model-ready content, plus code context.
Answers, not links — delegate questions to Perplexity's search-grounded models and get cited responses back.
Give your assistant a voice — text-to-speech, voice cloning and audio tools from the ElevenLabs API.
Give your assistant a real code sandbox — isolated cloud VMs for actually running the code it writes.
The ML hub in your context window — search models, datasets, papers and run Spaces from the official server.