AI for medical and scientific papers
If you already use Paperai, the paperai mcp server is the piece that lets your assistant work with it directly. AI for medical and scientific papers.
paperai is an AI application for medical and scientific papers.
The toolset is worth reading before you wire it up, because it tells you what the integration is really for:
name — DateCSV — Renders a CSV report. Columns and answers are extracted from articles with the results stored in a CSV fileAnnotation — Columns and answers are extracted from articles with the results annotated over the original PDF files. Requires passing in a path with the originalpaperai.report — Builds a report for a series of queries. For each query, the top scoring articles are shown along with matches from those articles. There is also apaperai.query — Runs a single query from the terminalpaperai.shell — Allows running multiple queries from the terminalNotebook — DescriptionApplication — DescriptionField — Descriptionoptions — RAG pipeline options - set the LLM, prompt templates, max length and morereport — Each unique top level parameter sets the report name. In the example above, it's called Researchquery — Vector query that identifies the top n documentsThe server ships on PyPI as paperai, so your MCP client can launch it on demand — there is no separate build step. Add the server block to your client's configuration, restart it, and the tools register themselves.
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. Paperai's toolset — name, CSV, Annotation and 11 more — is a fair guide to whether it matches your workflow. It is maintained by NeuML; worth a glance at recent repository activity before you build anything load-bearing on it.
This entry was verified against Paperai's own documentation before publication; SyncDev keeps the directory reviewed rather than auto-generated.
| Tool | What it does |
|---|---|
| name | Date |
| CSV | Renders a CSV report. Columns and answers are extracted from articles with the results stored in a CSV file. |
| Annotation | Columns and answers are extracted from articles with the results annotated over the original PDF files. Requires passing in a path with the original PDF files. |
| paperai.report | Builds a report for a series of queries. For each query, the top scoring articles are shown along with matches from those articles. There is also a highlights section showing the most relevant results. |
| paperai.query | Runs a single query from the terminal |
| paperai.shell | Allows running multiple queries from the terminal |
| Notebook | Description |
| Application | Description |
| Field | Description |
| options | RAG pipeline options - set the LLM, prompt templates, max length and more |
| report | Each unique top level parameter sets the report name. In the example above, it's called Research |
| query | Vector query that identifies the top n documents |
| columns | List of columns |
| Date | Article publication date |
{
"mcpServers": {
"paperai": {
"command": "uvx",
"args": ["paperai"]
}
}
}Add to claude_desktop_config.json, then restart Claude Desktop.
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.