About Paperswithcode
Paperswithcode helps machine-learning and AI researchers search papers, inspect details, and find related implementations. Its data comes via Hugging Face Papers and includes trending papers and linked repositories, models, datasets, and demo Spaces.
Key features
- 01Search machine-learning and AI papers
- 02View trending papers
- 03Retrieve abstracts and keywords
- 04Find implementations and related resources
Use cases
- 01Find papers on a research topic
- 02Browse papers trending today
- 03Review abstracts and linked datasets
- 04Locate code and models for a paper
MCP support
It exposes tools for paper search, details, and implementation lookup to MCP clients. It can connect through the Pipeworx remote gateway or run locally as a stdio MCP server.
Configuration
Remote servers need no local install. Pick your client, then copy the config or install in one click.
https://gateway.pipeworx.io/paperswithcode/mcpclaude mcp add --transport http pipeworx-io-paperswithcode https://gateway.pipeworx.io/paperswithcode/mcp{"mcpServers":{"pipeworx-io-paperswithcode":{"type":"http","url":"https://gateway.pipeworx.io/paperswithcode/mcp"}}}Open in Cursor{"servers":{"pipeworx-io-paperswithcode":{"type":"http","url":"https://gateway.pipeworx.io/paperswithcode/mcp"}}}Open in VS Code{"mcpServers":{"pipeworx-io-paperswithcode":{"type":"http","url":"https://gateway.pipeworx.io/paperswithcode/mcp"}}}{"mcpServers":{"pipeworx-io-paperswithcode":{"serverUrl":"https://gateway.pipeworx.io/paperswithcode/mcp"}}}Run commands in a terminal; paste JSON into the client’s MCP config and restart. Servers that need sign-in will prompt on first connect.
Tools
Details
From the project READMEmcp-paperswithcode
Papers (ML research) MCP.
Part of Pipeworx — an MCP gateway connecting AI agents to 1683+ live data sources.
Tools
| Tool | Description |
|---|---|
search_papers | Search machine-learning / AI research papers (via Hugging Face Papers, the successor to Papers with Code). Returns arXiv id, title, authors, community upvotes, and a linked GitHub repo when available. Use for "papers on ", "recent ML research about X". |
trending_papers | Today's trending ML/AI papers (or a given day's), ranked by community upvotes, via Hugging Face Papers. Use for "what are the hot AI papers", "trending ML research", "top papers this week". |
get_paper | Get full detail for a paper by its arXiv id (e.g. "2312.00752"): title, authors, abstract, AI-generated summary and keywords, community upvotes, and counts of linked models / datasets / demo Spaces. |
get_repositories | Find IMPLEMENTATIONS of a paper (by arXiv id): the linked GitHub repository plus the most popular Hugging Face models, demo Spaces, and datasets that implement or reproduce it — the "papers with code" view. Use for "code/implementation for ", "models trained on ". |
Quick Start
Add to your MCP client (Claude Desktop, Cursor, Windsurf, etc.):
{
"mcpServers": {
"paperswithcode": {
"url": "https://gateway.pipeworx.io/paperswithcode/mcp"
}
}
}
What this endpoint actually serves
tools/list at https://gateway.pipeworx.io/paperswithcode/mcp returns the tools in the table
above plus the shared Pipeworx meta-tools — ask_pipeworx,
discover_tools, search_within, remember/recall and the rest of the
gateway-wide set. So the tool count you see is larger than this table: a
single-pack endpoint currently lists roughly 30 shared tools alongside the
pack's own. The connection's initialize response states its exact scope, and
is the authoritative answer for a given day.
This is deliberate, not multiplexing by accident. The meta-tools are what let a
scoped connection answer a question this pack does not cover — via
ask_pipeworx, which routes across the whole catalog — without you adding a
second MCP server. There is currently no way to mount a pack endpoint without
them; if the extra schemas cost you more context than the routing is worth,
connect to the full gateway once rather than to several pack endpoints.
Or connect to the full Pipeworx gateway to get every pack's tools listed directly, instead of just this one's:
{
"mcpServers": {
"pipeworx": {
"url": "https://gateway.pipeworx.io/mcp"
}
}
}
Both URLs reach the same gateway and the same 1683+ data sources. The
only difference is which pack's tools are listed directly; ask_pipeworx
reaches all of them from either one.
No MCP client? Call it over HTTP
curl -X POST https://gateway.pipeworx.io/v1/tools/paperswithcode_search_papers \
-H 'Content-Type: application/json' \
-d '{"query":"attention transformer"}'
No account needed for the first calls. Inspect any tool: GET https://gateway.pipeworx.io/v1/tools/paperswithcode_search_papers. Find one: POST https://gateway.pipeworx.io/v1/tools/search_packs with {"query":"..."}.
Standalone (no gateway account)
This package also runs as a local stdio MCP server — no Pipeworx account, no gateway round-trip:
{
"mcpServers": {
"paperswithcode": {
"command": "npx",
"args": ["-y", "@pipeworx/mcp-paperswithcode"]
}
}
}
Or run it directly to confirm it starts:
npx -y @pipeworx/mcp-paperswithcode
It speaks MCP over stdin/stdout and answers initialize/tools/list/tools/call
for only this pack's tools — none of the shared meta-tools the gateway
connection above adds. Same source, same tools, no ask_pipeworx routing.
Using with ask_pipeworx
Instead of calling tools directly, you can ask questions in plain English — this works on the pack endpoint above as well as on the full gateway:
ask_pipeworx({ question: "your question about Paperswithcode data" })
The gateway picks the right tool and fills the arguments automatically.
More
License
MIT
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.