AI-powered code-review toolkit: MCP server + CLI to analyze GitHub PRs with local LLM smells, cloud LLM summaries, inline comments, risk gating, and test stub generation.
Documentation
CodeView MCP 🪄
_Powered by MCP, CodeLlama-13B (local), Llama-3.1-8b-instant (cloud)_
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1 Why
Modern PRs are huge—security issues or performance regressions slip through.
ReviewGenie does a 30-second AI review:
- Static regex rules → critical smells
- Local LLM → quick heuristics (no cloud cost)
- Cloud LLM → human-style summary & risk score
- Inline comments you can accept or ignore with one click
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2 What it does
| Tool | Purpose | Typical latency |
|---|---|---|
ping | Sanity check: show title/author/state | 0.3 s |
ingest | Fetch diff JSON + SQLite cache | 1–2 s |
analyze | Summary, smells[], rule_hits[], risk_score ∈ [0–1] | 6–10 s |
inline | Posts or previews comments | 0.5 s |
check | CI gate (risk_score > threshold) | 0.2 s |
generate_tests | Stub pytest files + open PR | 4–6 s |
Privacy note: only the diff snippet is sent to Groq; full code never leaves your machine.
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3 Quick Start (5 min)
git clone https://github.com/mann-uofg/codeview-mcp.git
cd codeview-mcp
python -m venv .venv && source .venv/bin/activate
pip install -r requirements.txt
pip install -e .
# one-liner smoke
reviewgenie/codeview ping https://github.com/psf/requests/pull/6883Store secrets once (env-var OR keyring):
from codeview_mcp.secret import set_in_keyring
set_in_keyring("GH_TOKEN", "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") #GitHub PAT
set_in_keyring("OPENAI_API_KEY", "gsk_xxxxxxxxxxxxxxxxxxxxxxxxxxxxxx") # Groq/OpenAI key
set_in_keyring("OPENAI_BASE_URL", "https://api.groq.com/openai/v1")Full tutorial: [docs/QUICKSTART.md](docs/QUICKSTART.md)
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4 Architecture
- SQLite → diff cache (24 h)
- ChromaDB → hunk embeddings
- Back-off → GitHub retries (403/5xx)
- Tracing → OpenTelemetry spans
- Detailed diagram: [
docs/ARCHITECTURE.md](docs/ARCHITECTURE.md)
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5 Benchmark
See [bench/benchmarks.md](bench/benchmarks.md):
10 popular OSS PRs → avg ⏱ 8.1 s analyze, 💰 \$0.0008 Groq cost, 96 % comment acceptance.
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6 Docs
- API schema: [
docs/API_SCHEMA.json](docs/API_SCHEMA.json) - CLI reference: [
docs/USAGE.md](docs/USAGE.md) - Config & env: [
docs/CONFIGURATION.md](docs/CONFIGURATION.md) - Contributing: [
docs/CONTRIBUTING.md](docs/CONTRIBUTING.md)
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7 Day-by-Day Log
| Day | Highlight |
|---|---|
| 0 | Project skeleton, MCP “hello” |
| 1 | GitHub ingest + diff cache |
| 2 | Local LLM smells + cloud risk |
| 3 | Inline locator + ChromaDB |
| 4 | CLI wrapper + risk gate |
| 5 | Stub test generator |
| 6 | Vector de-dup fix, CI passing |
| 7 | bench.py: eval & markdown report |
| 8 | Secrets via keyring, back-off, OpenTelemetry |
| 9 | Full docs suite & OpenAPI schema |
Full changelog: [docs/CHANGELOG.md](docs/CHANGELOG.md)
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8 Roadmap
- 🚦 Live GitHub Action auto-labels “High-Risk” PRs
- 🖼 Web UI with trace explorer
- 🐳 (Optional) Docker image for k8s / GHCR
- 🕵️♂️ Multi-language support (Go, Rust)
Star the repo ⭐ & drop an issue if you’d like to help!
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