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.