mcp-dryad
Click on "Deploy 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., "@mcp-dryadfind datasets about coral bleaching"
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.
mcp-dryad
Dryad (datadryad.org) MCP — keyless.
Part of Pipeworx — an MCP gateway connecting AI agents to 1683+ live data sources.
Tools
Tool | Description |
| Search Dryad, a curated repository of open research datasets (mostly the data underlying published, peer-reviewed studies in any field of science). Full-text query over titles, abstracts, authors and keywords. Returns matching datasets with DOI, title, authors, truncated abstract, keywords, publication date, field of science and license. Keyless. |
| Fetch a single Dryad dataset by its DOI (e.g. "doi:10.5061/dryad.hx3ffbgjj" or "10.5061/dryad.hx3ffbgjj"). Returns full metadata — title, authors, abstract, keywords, publication date, field of science, license, version — plus a download URL for the dataset files. Keyless. |
Related MCP server: mcp-openaire
Quick Start
Add to your MCP client (Claude Desktop, Cursor, Windsurf, etc.):
{
"mcpServers": {
"dryad": {
"url": "https://gateway.pipeworx.io/dryad/mcp"
}
}
}What this endpoint actually serves
tools/list at https://gateway.pipeworx.io/dryad/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/dryad_search_datasets \
-H 'Content-Type: application/json' \
-d '{"query":"coral bleaching"}'No account needed for the first calls. Inspect any tool: GET https://gateway.pipeworx.io/v1/tools/dryad_search_datasets. 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": {
"dryad": {
"command": "npx",
"args": ["-y", "@pipeworx/mcp-dryad"]
}
}
}Or run it directly to confirm it starts:
npx -y @pipeworx/mcp-dryadIt 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 Dryad data" })The gateway picks the right tool and fills the arguments automatically.
More
License
MIT
This server cannot be deployed
Maintenance
Related MCP Connectors
A public commons for agents to search and share reusable findings and open research questions.
Open scientific and engineering knowledge for AI agents: search, evidence, document publishing.
Machine-readable utilities and datasets for AI agents.
Search, sample and query open reproducible datasets published as immutable Parquet with schemas.
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