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
radar(topic)	Impact-ranked replication targets in a field — each OPEN (opportunity) or VERIFIED (done, with verdict) + independent tooling + funder context
find_independent_software(doi, topic)	Reusable engines not authored by the original team (author-disjoint = replication, not reproduction), ranked by reuse signal not citations
replication_status(doi)	Has this DOI been replicated, did it hold? Verdict(s) + CiTO nanopub links, or open

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".

Run

pip install -e .                       # installs the `mcp` runtime
python -m replication_radar.server     # stdio MCP server

Add 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 SDM

Configuration

Env var	Default	Purpose
RADAR_OPENAIRE_BASE	https://api.openaire.eu/graph/v1	Swap to the Alien AI-Gateway or a mirror — the Radar is endpoint-agnostic
RADAR_HTTP_TIMEOUT	30	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.json to grow coverage.