Replication Radar
Click on "Install 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., "@Replication Radarfind replication targets in ecology"
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.
Replication Radar
An MCP server that makes the OpenAIRE Graph more useful for replication. Point it at a research field or a paper and it answers the question the Graph structurally cannot: what high-impact work is worth replicating, is there independent reusable tooling to do it, and has it already been checked — with what verdict?
OpenAIRE's only value signal is citation-popularity (BIP! influence / popularity / impulse, classes C1–C5) — paper-bound, and orthogonal to whether a claim is true. The Radar joins three sources to add a replication layer on top:
OpenAIRE Graph — impact-ranks candidate papers (
api.openaire.eu/graph/v1).Software Heritage + repo signals — surfaces reusable method software.
Science Live nanopub verdicts — the "already checked → did it hold" overlay.
OpenAIRE AI Hackathon · Theme B (Build) · CC-BY. Built to be reused through the forrt-replication-template: discovery at the start of a replication, where the template's existing skills handle the nanopub chain at the end.
Tools
Tool | What it answers |
| Impact-ranked replication targets in a field — each OPEN (opportunity) or VERIFIED (done, with verdict) + independent tooling + funder context |
| Reusable engines not authored by the original team (author-disjoint = replication, not reproduction), ranked by reuse signal not citations |
| Has this DOI been replicated, did it hold? Verdict(s) + CiTO nanopub links, or |
The reproduction-vs-replication distinction, made computable
A reproduction re-runs the original code; a replication tests the same claim by a
different route. So the Radar filters tooling by author-disjointness from the
original paper — e.g. for Phillips et al. 2009, the dismo package (co-authored by
Phillips & Elith) is flagged rooted / non-independent, while biomod2 and jSDM
are independent. That filter is the difference between the two, and it's the thing
that makes this replication-aware rather than just "find the code".
Related MCP server: openreview-mcp
Run
pip install -e . # installs the `mcp` runtime
python -m replication_radar.server # stdio MCP serverAdd to an MCP client (.mcp.json):
{ "mcpServers": {
"replication-radar": { "command": "python", "args": ["-m", "replication_radar.server"] }
} }The core (OpenAIRE client + radar logic) is stdlib-only — try it without the MCP runtime:
PYTHONPATH=src python3 demo_sdm.py # live vertical-slice demo on SDMConfiguration
Env var | Default | Purpose |
|
| Swap to the Alien AI-Gateway or a mirror — the Radar is endpoint-agnostic |
|
| Per-request timeout (s) |
Known limits (v1, honest)
Keyword-bound discovery. OpenAIRE free-text terms are AND-ed; long queries return nothing. Use short topics. The VERIFIED overlay is guaranteed (resolved from the verdict index directly), but OPEN-target recall depends on the query.
No graph-relation traversal on the public API (paper→its software/data/grant edges aren't exposed): tooling/data are matched heuristically by topic + author independence, not by a hard relation. Upgrades cleanly if a gateway exposes relations.
Funder context is field-level, not per-paper (per-paper funder attribution is not reachable); budgets are frequently reported as 0 in records.
The verdict index ships 6 source works / 12 chains (Science Live). Extend
data/verdicts.jsonto grow coverage.
Maintenance
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