openreview-mcp
MCP server for OpenReview — search submissions, fetch reviews, meta-reviews, rebuttals, and decisions from NeurIPS, ICLR, ACL ARR, COLM, TMLR, and any other venue hosted on OpenReview.
Built by OpenCódice Research. The design rationale, analysis pipeline, and ICLR 2024 case study are documented in the OpenCódice Technical Report OC-TR-2026-007 (Zenodo, DOI 10.5281/zenodo.19758460).
Why
The academic MCP ecosystem covers arXiv (academia-mcp), Semantic Scholar, and HuggingFace, but the richest source of peer-review signal — OpenReview — is missing. This server exposes reviews, scores, area-chair decisions, and author rebuttals as MCP tools, enabling:
- Reviewer-style critique agents grounded in real reviewer language
- Bibliography verification for workshop and ARR venues
- Weakness pattern analysis across a venue/year ("what does NeurIPS 2025 tend to reject for?")
- Meta-review study for understanding area-chair decision patterns
Tools
| Tool | Purpose |
|---|---|
openreview_list_venues |
List OpenReview venues, filterable by year or series |
openreview_venue_stats |
Acceptance rate and score distribution for a venue |
openreview_search_submissions |
Search papers by venue/query/author/keywords |
openreview_get_submission |
Full metadata + abstract + PDF URL for a submission |
openreview_search_by_author |
All submissions by an author profile |
openreview_get_reviews |
All reviews (scores, confidence, strengths, weaknesses) |
openreview_get_meta_review |
Area-chair meta-review and recommendation |
openreview_get_rebuttal |
Author responses to reviewers |
openreview_get_decision |
Accept/reject decision and comment |
openreview_get_profile |
Author profile, affiliation, publications |
openreview_aggregate_weaknesses |
Cluster recurrent reviewer complaints across a venue's rejections (requires [analysis] extra) |
Install
pip install openreview-mcp
# or, with the weakness-clustering tool enabled:
pip install "openreview-mcp[analysis]"
Signature tool: openreview_aggregate_weaknesses
Ask the server to cluster reviewer weakness themes across a venue's rejections:
> Cluster 50 rejected ICLR 2024 submissions by weakness theme (k=10).
Returns clusters with top TF-IDF terms, three representative exemplar snippets per cluster, and the contributing submission ids. The consuming LLM (Claude) labels each cluster from the evidence, so no fixed taxonomy is baked into the server.
See the ICLR 2024 case study for a full reproducible analysis of 100 rejected submissions, and the launch post openreview-mcp: peer review as a queryable resource for LLMs for the design rationale and a narrative tour of the same data.
Configuration
The server works out of the box for public venues. For access to venues requiring login:
export OPENREVIEW_USERNAME="you@example.com"
export OPENREVIEW_PASSWORD="..."
Use with Claude Code
claude mcp add openreview -- openreview-mcp
Use with Claude Desktop
Add to ~/Library/Application Support/Claude/claude_desktop_config.json:
{
"mcpServers": {
"openreview": {
"command": "openreview-mcp"
}
}
}
See examples/claude_desktop_config.json for a full example.
Run as HTTP server
openreview-mcp --http --port 8000
Development
make install # uv sync with dev extras
make test # pytest (uses VCR cassettes, no network)
make lint # ruff + mypy
make serve # run HTTP server locally on :8000
License
MIT — see LICENSE.
Metadata
Release files for openreview-mcp 0.1.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | Provenance | |
|---|---|---|---|---|
| openreview_mcp-0.1.0.tar.gz | 26.7 kB | With provenance | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| openreview_mcp-0.1.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 46.3 kB
Release files / openreview_mcp-0.1.0.tar.gz
| Download URL | openreview_mcp-0.1.0.tar.gz |
|---|---|
| Size | 26.7 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
d1f700a2597433a3ad471932c088a15e7c2824358cdfade9b2cf350674c17c2c
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Transparency logRelease files / openreview_mcp-0.1.0-py3-none-any.whl
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| Tags | Python 3 |
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SHA-256 checksum How to use checksums |
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Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/6.1.0 CPython/3.13.12
|
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PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Apr 25, 2026.
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Attestation for openreview_mcp-0.1.0.tar.gz
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Apr 25, 2026.
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