Open Code Review— mcp server

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<p align="center"> <strong>Customizable multi-agent code review for AI-assisted development</strong> </p>

<p align="center"> <a href="https://www.npmjs.com/package/@open-code-review/cli"><img src="https://img.shields.io/npm/v/@open-code-review/cli.svg" alt="npm version"></a> <a href="https://github.com/spencermarx/open-code-review/blob/main/LICENSE"><img src="https://img.shields.io/badge/license-Apache--2.0-blue.svg" alt="License"></a> </p>

Quick Start

Prerequisites: Node.js >= 22.5, Git, and an AI coding assistant.

# 1. Install the CLI globally
npm install -g @open-code-review/cli

# 2. Initialize OCR in your project
cd your-project
ocr init

# 3. Launch the dashboard and run your first review
ocr dashboard

Your browser will open the OCR dashboard — you're ready to run your first review.

Any package manager works. From v2.1 OCR uses Node's built-in SQLite (node:sqlite) — there's no native module to compile, so npm i -g, pnpm add -g, and yarn global add all install cleanly (including under pnpm 10+, which blocks dependency build scripts). It just needs Node >= 22.5.

ocr init detects your installed AI tools (Claude Code, Cursor, Windsurf, and 11 more) and configures each one automatically. Then open the dashboard to run a review, browse results, and manage your workflow from the browser.

Or run reviews directly from your AI coding assistant:

/ocr:review                                    # Claude Code / Cursor
/ocr-review                                    # Windsurf / other tools
/ocr-review Review against openspec/spec.md    # With requirements

Why Open Code Review?

When you ask an AI to "review my code," you get a single perspective — one pass, one set of priorities. OCR changes that fundamentally:

• Multi-agent redundancy — Multiple reviewer instances examine your code independently. Different attention patterns catch different issues. What one reviewer misses, another finds.
• Discourse before synthesis — Reviewers don't just produce findings — they debate them. They challenge assumptions, validate concerns, and surface questions no single reviewer would ask.
• Fully customizable teams — Pick from 28 reviewer personas (including famous engineers like Martin Fowler, Kent Beck, and Sandi Metz), create your own persistent reviewers, or describe ephemeral one-off reviewers inline.
• Requirements-aware — Pass in a spec, proposal, or acceptance criteria. Every reviewer evaluates the code against your stated requirements, not just general best practices.
• Project context — OCR discovers your standards from CLAUDE.md, .cursorrules, OpenSpec configs, and other common patterns. Reviewers apply your conventions.

                    ┌─────────────┐
                    │  Tech Lead  │  ← Orchestrates the review
                    └──────┬──────┘
                           │
         ┌─────────────────┼─────────────────┐
         │                 │                 │
         ▼                 ▼                 ▼
┌─────────────────┐ ┌─────────────┐ ┌─────────────────┐
│   Your Team     │ │  Your Team  │ │   Your Team     │
│   Composition   │ │  Composition│ │   Composition   │
└─────────────────┘ └─────────────┘ └─────────────────┘
         │                 │                 │
         └─────────────────┼─────────────────┘
                           │
                    ┌──────▼──────┐
                    │  Discourse  │  ← Reviewers debate findings
                    └──────┬──────┘
                           │
                    ┌──────▼──────┐
                    │  Synthesis  │  ← Unified, prioritized feedback
                    └─────────────┘

Note: OCR does not replace human code review. The goal is to reduce the burden on human reviewers by catching issues earlier — so human review is faster and more focused on things machines can't catch.

Table of Contents

• The Dashboard
• IDE & CLI Workflows
• Features
• Multi-Agent Review
• Multi-Model Teams
• Code Review Maps
• Requirements-Aware Review
• Reviewer Discourse
• GitHub PR Posting
• Multi-Round Reviews
• Custom Reviewers
• Famous Engineer Personas
• Ephemeral Reviewers
• Real-Time Progress
• Address Feedback
• Session Notes & Chat
• Configuration
• Commands Reference
• Updating OCR
• Supported AI Tools
• Requirements

The Dashboard

The dashboard is the recommended way to run reviews, browse results, and manage your workflow. Launch it with ocr dashboard.

Run reviews and maps

The Command Center lets you launch multi-agent code reviews and Code Review Maps directly from the dashboard. Specify targets, add requirements, toggle fresh starts — then watch live terminal output as agents work.

<p align="center"> <img src="assets/ocr-tool-command-center.png" alt="OCR Dashboard Command Center" width="700" /> </p>

Browse and triage reviews

View verdict banners, individual reviewer cards, findings tables, and cross-reviewer discourse for every review round. Set triage status on findings (needs review, in progress, changes made, acknowledged, dismissed) with filtering and sorting.

<p align="center"> <img src="assets/ocr-tool-focused-review.png" alt="OCR review output with findings" width="700" /> </p>

Explore Code Review Maps

Navigate large changesets with section-based breakdowns, rendered Mermaid dependency graphs, and file-level progress tracking.

<p align="center"> <img src="assets/ocr-tool-focused-code-review-map.png" alt="OCR Code Review Map" width="700" /> </p>

Post to GitHub

Two posting modes from the review round page:

• Post Team Review — Posts the multi-reviewer synthesis as-is
• Generate Human Review — AI-rewrites all findings into a single, natural human voice following Google's code review guidelines. Preview, edit, and save drafts before posting.

<p align="center"> <img src="assets/ocr-tool-translate-to-human-review-button.png" alt="Post Review to GitHub dialog" width="700" /> </p>

<p align="center"> <img src="assets/ocr-tool-example-translated-human-review.png" alt="Human-voice review posted to GitHub PR" width="700" /> </p>

Manage your review team

The Team page lets you browse all 28 reviewer personas grouped by tier (Generalists, Specialists, Famous Engineers, Custom). View full prompts, focus areas, and persona details. Create new reviewers or sync metadata — all from the dashboard.

<p align="center"> <img src="assets/ocr-tool-manage-reviewers-team.png" alt="OCR Team page for managing reviewer personas" width="700" /> </p>

Set your default team composition

Pick which personas show up on every review and how many instances of each — your default lineup, persisted to .ocr/config.yaml.

<p align="center"> <img src="assets/ocr-default-team-composition.png" alt="OCR default team composition editor" width="700" /> </p>

Assign models per reviewer

Different reviewers, different models. Pair a fast model on a generalist with a deeper model on a specialist, mix vendors across a single team, or set a workspace-wide default. The dashboard discovers your installed vendor (Claude Code or OpenCode) and lists every model it offers.

<p align="center"> <img src="assets/ocr-default-reviewer-model-configuration.png" alt="Per-reviewer model configuration on the Team page" width="700" /> </p>

Override the team for a single review

Need a heavier-hitting model for one risky changeset? The Command Center lets you swap personas and models per-review without touching your saved defaults.

<p align="center"> <img src="assets/ocr-per-review-model-configuration.png" alt="Per-review model configuration in the Command Center" width="700" /> </p>

Address Feedback

After reviewing findings, address them directly. Copy a portable AI prompt into any coding tool, or — with Claude Code or OpenCode detected — run an agent directly from the dashboard to corroborate, validate, and implement changes.

Ask the Team

AI-powered chat on every review round and map run page. Ask follow-up questions about specific findings, request clarification on reviewer reasoning, or explore alternative approaches.

The dashboard reads from the same .ocr/ directory and SQLite database used by the review workflow. No separate configuration is needed.

IDE & CLI Workflows

Run all OCR commands directly from your AI coding assistant using slash commands.

Start a review

git add .

Then in your AI assistant:

/ocr-review                                     # Windsurf / flat-prefix tools
/ocr:review                                     # Claude Code / Cursor
/ocr-review Review against openspec/spec.md     # With requirements context

Watch progress in real-time

In a separate terminal:

ocr progress

  Open Code Review

  2026-01-26-main  ·  Round 2  ·  1m 23s

  ━━━━━━━━━━━━━━━━────────  60%  ·  Parallel Reviews

  ✓ Context Discovery
  ✓ Change Context
  ✓ Tech Lead Analysis
  ▸ Parallel Reviews
    Round 2
    ✓ Principal #1 2  │  ✓ Principal #2 1  │  ○ Quality #1 0  │  ○ Quality #2 0
    Round 1 ✓ 4 reviewers
  · Aggregate Findings
  · Reviewer Discourse
  · Final Synthesis

  Ctrl+C to exit

More AI assistant commands

/ocr-map                   # Generate a Code Review Map for large changesets
/ocr-post                  # Post review to GitHub PR
/ocr-doctor                # Verify installation
/ocr-reviewers             # List available reviewer personas
/ocr-history               # List past review sessions
/ocr-show [session]        # Display a specific past review

For Claude Code / Cursor, use /ocr:review, /ocr:map, /ocr:post, etc.

Features

Multi-Agent Review

OCR follows an 8-phase workflow orchestrated by a Tech Lead agent:

Multi-Model Teams

Different reviewers can run on different models. Pair a fast generalist on Sonnet with a deep specialist on Opus, share a single model across a redundancy pair, or define personal aliases like workhorse/big-brain so config reads naturally.

# .ocr/config.yaml
models:
  aliases:
    workhorse: claude-sonnet-4-6
    big-brain: claude-opus-4-7
  default: claude-sonnet-4-6   # used when an instance has no explicit model

default_team:
  # Form 1 — shorthand: N instances, default model
  quality: 2

  # Form 2 — object: N instances, all sharing one model
  security: { count: 1, model: big-brain }

  # Form 3 — list: per-instance model and optional name
  principal:
    - { model: big-brain }
    - { model: workhorse, name: principal-balanced }

Override per-review from the Command Center, or via --team on the command line. The dashboard auto-discovers every model your installed vendor (Claude Code or OpenCode) offers — no need to memorize identifiers.

Code Review Maps

For large changesets (20+ files), Code Review Maps provide a structured navigation document — grouping related changes into sections, identifying key files, and surfacing dependencies with Mermaid diagrams.

/ocr-map                           # Map staged changes
/ocr-map HEAD~10                   # Map last 10 commits
/ocr-map feature/big-refactor      # Map branch vs main
/ocr-map --requirements spec.md    # Map with requirements context

Three specialized agents (Map Architect, Flow Analyst, Requirements Mapper) run with configurable redundancy. Browse maps in the dashboard with dependency graphs and file-level progress tracking.

When to use a map: Large changesets where you'd spend time figuring out where to start, or to orient a teammate before they dive into the code. For most changesets, /ocr-review alone is sufficient.

Requirements-Aware Review

OCR is most powerful when reviewing code against explicit requirements:

/ocr-review Review against openspec/specs/cli/spec.md        # Spec file
/ocr-review Check against openspec/changes/add-auth/proposal.md  # Proposal
/ocr-review Requirements:                                     # Inline
- Max 100 requests per minute per user
- Return 429 with Retry-After header
/ocr-review This fixes BUG-1234 where users bypassed rate limiting  # Ticket

Requirements propagate to all reviewers. The final synthesis includes a Requirements Verification table showing which requirements are met, which have gaps, and any ambiguities.

Reviewer Discourse

Before producing the final review, reviewers examine each other's findings in a structured debate:

• AGREE — Validate findings with supporting evidence
• CHALLENGE — Question assumptions or reasoning
• CONNECT — Relate findings across reviewers
• SURFACE — Raise new concerns from the debate

This catches false positives, strengthens valid findings, and surfaces issues that no single reviewer would find alone.

GitHub PR Posting

Post reviews directly to your PR from the dashboard or your AI assistant:

/ocr-post

Two modes:

• Team Review — Posts the multi-reviewer synthesis as-is
• Human Review Translation — AI-rewrites findings into a natural, first-person voice following Google's code review guidelines. Preview, edit, and save drafts before posting.

<p align="center"> <img src="assets/github-code-review-example.png" alt="OCR review posted to GitHub PR" width="700" /> </p>

Prerequisites: GitHub CLI (gh) installed and authenticated, open PR on current branch.

Multi-Round Reviews

OCR supports iterative review cycles:

Running /ocr-review on an existing session checks if the current round is complete. If complete, it starts a new round. Previous rounds are preserved — shared context (discovered-standards.md, context.md) is reused across rounds.

Custom Reviewers

OCR ships with 28 reviewer personas across four tiers — generalists, specialists, famous engineers, and custom. Browse and manage them from the dashboard's Team page, or create your own.

Three ways to build your review team:

Create a custom reviewer from your AI assistant:

/ocr:create-reviewer api-design --focus "REST API design, backwards compatibility, versioning, error response consistency"

This generates a reviewer file following the standard template, syncs metadata, and makes it available in the dashboard immediately.

Override your team per-review with the --team flag:

/ocr:review --team principal:2,security:1,martin-fowler:1

Famous Engineer Personas

Review your code through the lens of industry legends. OCR includes personas for Martin Fowler, Kent Beck, Sandi Metz, Rich Hickey, Kent Dodds, Anders Hejlsberg, John Ousterhout, Kamil Mysliwiec, Tanner Linsley, and Vladimir Khorikov — each with review styles, philosophies, and focus areas reflecting their published work.

<p align="center"> <img src="assets/ocr-tool-famous-engineer-reviewers.png" alt="Famous Engineer reviewer personas on the Team page" width="700" /> </p>

Add them to your team like any other reviewer:

# .ocr/config.yaml
default_team:
  principal: 2
  martin-fowler: 1
  sandi-metz: 1

Ephemeral Reviewers

Need a one-off review perspective without creating a persistent reviewer? Describe it inline with --reviewer:

/ocr:review --reviewer "Focus on error handling in the auth flow"
/ocr:review --team principal:2 --reviewer "Review as a junior developer would" --reviewer "Check accessibility compliance"

Ephemeral reviewers participate in all review phases (parallel reviews, discourse, synthesis) just like library reviewers. They don't persist to the reviewer library — they exist only for that review session.

Real-Time Progress

The ocr progress command shows a live terminal UI with phase tracking, elapsed time, reviewer status, finding counts, and completion percentage. It auto-detects the latest active session or accepts a --session flag.

Address Feedback

After a review, use the /ocr-address command or the dashboard's "Address Feedback" button to spawn an AI agent that corroborates each finding against actual code, validates the suggestions, and implements the changes — with human approval at each step.

Session Notes & Chat

The dashboard supports session-level notes for tracking follow-up items and AI-powered chat on review rounds and map runs for asking follow-up questions about findings.

Configuration

After running ocr init, edit .ocr/config.yaml:

# Project context injected into all reviews
context: |
  Tech stack: TypeScript, React, Node.js
  Critical: All public APIs must be backwards compatible

# Customize your reviewer team composition
# Three forms — see "Multi-Model Teams" above for the full variants.
default_team:
  principal: 2       # Architecture, design patterns
  quality: 2         # Code style, best practices
  # security: 1      # Auto-added for auth/data changes
  # testing: 1       # Auto-added for logic changes
  # martin-fowler: 1 # Famous engineer persona

# Optional: model aliases + workspace default
# models:
#   aliases:
#     workhorse: claude-sonnet-4-6
#     big-brain: claude-opus-4-7
#   default: claude-sonnet-4-6

# Context discovery
context_discovery:
  openspec:
    enabled: true
  references:
    - "CLAUDE.md"
    - ".cursorrules"
    - "CONTRIBUTING.md"

# Code Review Map tuning
code-review-map:
  agents:
    flow_analysts: 2
    requirements_mappers: 2

# Discourse
discourse:
  enabled: true

# GitHub posting
github:
  auto_prompt_post: false
  comment_format: "single"

# Dashboard IDE integration
dashboard:
  ide: auto  # vscode | cursor | windsurf | jetbrains | sublime

Team composition can also be changed per-review via --team (explicit roster) or --reviewer (ephemeral reviewers), or via natural language: "use 3 principal reviewers and add security."

Commands Reference

AI Assistant Commands

For Claude Code / Cursor, use `/ocr:review`, `/ocr:map`, etc.

CLI Commands

Updating OCR

After upgrading the package, run ocr update to sync your project:

npm i -g @open-code-review/cli@latest
ocr update

ocr update --dry-run     # Preview changes
ocr update --commands    # Commands only
ocr update --skills      # Skills and references only
ocr update --inject      # AGENTS.md/CLAUDE.md only

Supported AI Tools

ocr init detects and configures all of these automatically:

Requirements

• Node.js >= 22.5.0
• Git — For diff analysis
• An AI coding assistant — Claude Code, Cursor, Windsurf, or any supported tool
• GitHub CLI (gh) — Optional, for posting reviews to PRs

v2.1 — built-in SQLite engine: OCR stores state with Node's built-in SQLite (node:sqlite, on-disk + WAL) for durable, concurrent-safe writes — no native module, no prebuilt binary, no install script. It installs cleanly under any package manager (npm, pnpm 10+, yarn) on every platform. This requires Node >= 22.5 (when node:sqlite landed); on an older Node the CLI prints a clear upgrade message. Run ocr doctor to verify the engine.

>

Upgrading from v1.x or v2.0? Make sure you're on Node >= 22.5, quit any running ocr dashboard, then npm i -g @open-code-review/cli@latest (or pnpm add -g …). Your existing database upgrades automatically on the next ocr command: a backup is written to ocr.db.bak.v<n>, legacy sessions are reconciled, and a one-time notice summarizes what happened. No manual steps.

Run ocr doctor to verify your setup.

Important: The CLI (npm install -g @open-code-review/cli) is required for all OCR workflows. Both review and map commands use ocr state for progress tracking at every phase transition.

Session Storage

Reviews are persisted to .ocr/sessions/{date}-{branch}/:

.ocr/sessions/2026-01-26-feature-auth/
├── discovered-standards.md  # Merged project context (shared)
├── context.md               # Change summary + Tech Lead guidance (shared)
├── requirements.md          # User-provided requirements (shared, if any)
├── rounds/
│   ├── round-1/
│   │   ├── reviews/         # Individual reviewer outputs
│   │   │   ├── principal-1.md
│   │   │   ├── quality-1.md
│   │   │   └── ephemeral-1.md  # From --reviewer (if used)
│   │   ├── discourse.md     # Cross-reviewer discussion
│   │   └── final.md         # Synthesized final review
│   └── round-2/             # Created on re-review
└── maps/
    └── run-1/
        ├── map.md           # Code Review Map
        └── flow-analysis.md # Dependency graph (Mermaid)

Sessions are gitignored by default.

License

Apache-2.0

Links

• GitHub: github.com/spencermarx/open-code-review
• npm (CLI): @open-code-review/cli
• npm (Agents): @open-code-review/agents