Setup

Better Code Review Graph — Manual Setup Guide

2026-05-02 Update: Plugin install (Method 1) now uses pure stdio mode. API keys are optional env vars. The previous “Zero-Config Relay” auto-spawn pattern has been removed. If you relied on the relay form to enter API keys, please:

1. Set the env var directly in plugin config (Method 1), OR
2. Use HTTP self-host mode (advanced; out of scope of this guide).

Method overview

This plugin defaults to stdio via plugin install (uvx/npx) — the simplest path and the one this guide covers in full. It also ships Docker images (:stdio and :http targets) and supports HTTP transport (MCP_TRANSPORT=http / TRANSPORT_MODE=http / --http) for multi-user self-hosting. What it does not offer (unlike better-notion-mcp/better-email-mcp/better-telegram-mcp) is a hosted remote-relay/OAuth mode — HTTP here is self-host only.

For comparison, the other 6 plugins in this stack (better-notion-mcp, better-email-mcp, better-telegram-mcp, wet-mcp, mnemo-mcp, imagine-mcp) document 3 methods:

1. Default — Plugin install (uvx/npx) stdio
2. Fallback — Docker stdio (Windows/macOS PATH issues)
3. Recommended — Docker HTTP (multi-device, OAuth/relay form, claude.ai web)

⚠️ Mutually exclusive — pick ONE per plugin: Do NOT stack /plugin install AND a user mcpServers override (Docker stdio or HTTP) — both would load simultaneously and create duplicate entries (plugin’s npx/uvx stdio + your override). Plugin matching is by endpoint (URL or command string) per CC docs, not by name — and npx/uvx ≠ docker ≠ HTTP URL, so all three are distinct endpoints. Choosing the Docker stdio or HTTP self-host path means losing the plugin’s skills/agents/hooks/commands. For full plugin features, use the default plugin install (Method 1) documented below.

Prerequisites

• Python 3.13 (3.14+ is NOT supported)
• uv or uvx installed (docs)
• Docker (optional, for containerized setup)
• A code repository to analyze

Method 1: Claude Code Plugin (Recommended)

Plugin marketplace install runs the server in pure stdio mode with optional API key env vars. No daemon-bridge, no auto-spawn, no relay form. The graph is stored locally in SQLite — no external graph database required.

Credential prompts at install

When you run /plugin install, Claude Code prompts you for the following credentials (declared in userConfig per CC docs). Sensitive values are stored in your system keychain and persist across /plugin update:

Field	Required	Where to obtain
JINA_AI_API_KEY	Optional	https://jina.ai/api-key
GEMINI_API_KEY	Optional	https://aistudio.google.com/apikey
OPENAI_API_KEY	Optional	https://platform.openai.com/api-keys
COHERE_API_KEY	Optional	https://dashboard.cohere.com/api-keys

Steps

1. Open Claude Code.
2. Install the plugin (Claude Code prompts for JINA_AI_API_KEY — press Enter to skip):

   /plugin marketplace add n24q02m/claude-plugins
   /plugin install better-code-review-graph@n24q02m-plugins

3. The server starts automatically when Claude Code launches.
4. The SessionStart hook auto-builds the graph for the current project; PostToolUse updates it after edits.

Credential Setup

All API keys are optional. The server works with local ONNX embeddings out of the box.

Stdio Mode (Env Vars)

Set API keys in your MCP client env block or shell profile:

export JINA_AI_API_KEY="jina_..."
export GEMINI_API_KEY="AIza..."

Environment Variable Reference

Variable	Required	Default	Description
JINA_AI_API_KEY	No	—	Jina AI: embedding + reranking (highest priority)
GEMINI_API_KEY	No	—	Gemini: embedding (free tier). Also accepts GOOGLE_API_KEY
OPENAI_API_KEY	No	—	OpenAI: embedding
COHERE_API_KEY	No	—	Cohere: embedding + reranking. Also accepts CO_API_KEY
EMBEDDING_BACKEND	No	auto-detect	cloud or local (ONNX)
EMBEDDING_MODEL	No	auto-detect	Cloud embedding model name
TRANSPORT_MODE	No	stdio	Set to http to enable HTTP transport (multi-user).
PUBLIC_URL	Yes (http)	—	Server’s public URL for relay form.
MCP_DCR_SERVER_SECRET	Yes (http)	—	HMAC secret for stateless Dynamic Client Registration.
MCP_PORT	No	8080	Server port (http mode only).
LOG_LEVEL	No	INFO	Logging level

Embedding Provider Priority

Cloud auto-detection order: Jina AI > Gemini > OpenAI > Cohere > Local ONNX (Qwen3)

All embeddings are stored at 768 dimensions. Switching providers does NOT invalidate existing vectors.

Supported Languages

Python, TypeScript, JavaScript, Go, Rust, Java, C#, Ruby, Kotlin, Swift, PHP, C/C++, Solidity

Ignore Files

Create .code-review-graphignore in your project root to exclude paths:

generated/**
*.generated.ts
vendor/**
node_modules/**

Troubleshooting

Graph build finds no files

Ensure the repo_path parameter points to the root of a code repository. Check that the project contains files in a supported language.

First embedding is slow

On first use, the local ONNX embedding model (~570MB) is downloaded. Subsequent runs are instant. Use cloud embedding (any API key) to avoid this download.

”No graph found” error

Build the graph first:

graph(action="build", repo_path="/path/to/your/repo")

Docker cannot access repo files

Ensure the volume mount is correct. The repo path inside the container is /repo:

docker run -i --rm -v "/absolute/path/to/repo:/repo:ro" n24q02m/better-code-review-graph:latest