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Open Code Review

by raye-deng

MCP tools for scanning AI-generated code, explaining issues, and applying reviewed fixes.

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Install Open Code Review

Pick your client, then add it in one click or paste the config.

STDIONode.js
~/Library/Application Support/Claude/claude_desktop_config.json
claude_desktop_config.json
{
  "mcpServers": {
    "raye-deng-open-code-review": {
      "command": "npx",
      "args": [
        "-y",
        "@opencodereview/cli"
      ]
    }
  }
}

Settings → Developer → Edit Config, merge this into mcpServers, then fully quit and reopen Claude Desktop.

Install not yet sandbox-tested— we haven't run this command through our automated sandbox yet. Not a red flag; we're working through the catalog.

Overview

raye-deng/open-code-review MCP server brings AI-focused code review into MCP-compatible development clients. It exposes tools for quality scans, issue explanations, and AI-assisted fixes, using the project's local analysis engine and optional LLM providers. The server runs through npx and can be configured in Claude Desktop, Cursor, Windsurf, or VS Code. Reach for it when generated code needs checks for hallucinated packages, stale APIs, security patterns, or cross-file defects.

Use cases

  • Scan AI-generated source for hallucinated packages
  • Explain code-review findings inside an AI editor
  • Check generated code for stale APIs and security patterns
  • Apply reviewed AI-assisted fixes
  • Run deeper cross-file analysis with a local LLM

Key features

  • MCP tools for scanning, healing, and explaining issues
  • Hallucinated import and stale API detection
  • Structural, embedding, and LLM-based analysis
  • Ollama and OpenAI-compatible provider support
  • Language-specific checks across documented language families
  • CLI, CI/CD, JSON, SARIF, and HTML workflows

Security & health

What it can reach and how reliably it runs — from our own checks, not the maintainer’s claims.

ActiveLast checked Today

Recent health check succeeded.

9/13 checks healthy over the last 90d

Supply-chain signal

No high-severity advisories surfaced by our automated scan.

Critical 0High 0Medium 0Low 0

Scanned 6d ago via OSV.dev · @opencodereview/cli (npm)

Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks — not a rating.

GitHub stars40Stargazers on the source repository.
npm downloads61Package downloads in the last 30 days.
Last commit6mo agoMost recent push to the default branch.
Directory activity1 viewsConfig copies, upvotes, and views on AllMCPs.

How Open Code Review works

What raye-deng/open-code-review MCP server does

The raye-deng/open-code-review MCP server exposes Open Code Review's code-quality checks to AI development environments through Model Context Protocol. Its tools are ocr_scan, ocr_heal, and ocr_explain: the first evaluates a codebase, the second proposes or applies automated corrections, and the third explains detected issues.

The underlying project targets defects that conventional linters may not identify, including imports for packages that do not exist, APIs that are no longer current, contradictions spanning files, unnecessary abstractions, and some security anti-patterns. Language-specific checks are documented for TypeScript and JavaScript, Python, Java, Go, and Kotlin. Package verification covers registries including npm, PyPI, Maven, and Go modules.

How it works

A scan can use a fast structural mode or the deeper L3 workflow. The deeper workflow combines structural checks, embedding-based semantic analysis, and an LLM pass for cross-file coherence, logic defects, confidence scoring, and suggested fixes. Results include a quality score, severity information, and a pass or fail status against a configured threshold.

LLM-assisted analysis can run through Ollama or an OpenAI-compatible endpoint. This lets teams choose a local model or provide a remote model endpoint using the CLI's provider options. The MCP layer makes the review functions available to the connected client; the repository also offers a standalone CLI and CI/CD usage separately.

Setup and configuration

Run the raye-deng/open-code-review MCP server with:

Terminal
npx @opencodereview/mcp-server

For Claude Desktop, add an MCP entry whose command is npx and whose arguments request the package with -y. The README gives the same configuration pattern for Cursor, Windsurf, and VS Code Copilot MCP settings. The server is therefore suited to clients that can launch a standard command-based MCP server.

LLM settings are configured through the project's broader Open Code Review configuration and provider options. Ollama examples use models such as qwen3-coder; OpenAI-compatible examples specify an API base, model, and API key. A fast scan does not require AI, while deep analysis does require a configured model provider.

Tools and capabilities

  • ocr_scan: run the code-quality gate against source code.
  • ocr_heal: generate AI-assisted fixes, with the CLI supporting dry runs before changes are applied.
  • ocr_explain: provide more context about a reported issue.
  • Detect hallucinated imports, stale APIs, security patterns, over-engineering, and selected language-specific problems.
  • Use local or OpenAI-compatible LLM providers for deeper analysis.

The project also supports terminal, JSON, SARIF, and HTML output through its CLI, plus GitHub Actions and GitLab CI workflows. Those integrations are part of the wider project rather than separate MCP tools.

Limitations and notes

The raye-deng/open-code-review MCP server is an interface to the Open Code Review project, not a general-purpose code editor or repository host. Review generated changes before applying them; the project documents a dry-run option for its CLI healing workflow.

The repository is licensed under BUSL-1.1. The README names Claude Desktop, Cursor, Windsurf, VS Code Copilot, Claude Code, and Codex as compatible or supported environments, while the MCP setup shown uses a command-based launch configuration.

Read the full README View source on GitHub

Reviews

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Frequently Asked Questions about Open Code Review

raye-deng/open-code-review is an MCP server that connects AI coding clients to Open Code Review's analysis engine. Its main tools are `ocr_scan` for quality checks, `ocr_heal` for AI-assisted fixes, and `ocr_explain` for issue explanations. It can use local Ollama models or OpenAI-compatible providers for deeper analysis.

Run `npx @opencodereview/mcp-server`, then add that command to your MCP client's configuration.

Yes, Cursor is listed as a supported client. Add the same command-based MCP configuration used for the other supported environments to Cursor's MCP settings, then expose the `ocr_scan`, `ocr_heal`, and `ocr_explain` tools.

Yes, it can use Ollama for local analysis. The documented examples use Ollama with models such as `qwen3-coder`; OpenAI-compatible endpoints are also supported when you need a remote provider.

It detects issues including hallucinated imports, stale APIs, cross-file context artifacts, over-engineering, and security anti-patterns. The project also documents language-specific checks and registry verification for npm, PyPI, Maven, and Go modules.

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