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codeview-mcp

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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.

2 stars PythonDeveloper Kits Updated Sep 19, 2025

Documentation

CodeView MCP 🪄

_Powered by MCP, CodeLlama-13B (local), Llama-3.1-8b-instant (cloud)_

PyPI
CI

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

2 What it does

ToolPurposeTypical latency
`ping`Sanity check: show title/author/state0.3 s
`ingest`Fetch diff JSON + SQLite cache1–2 s
`analyze`Summary, smells[], rule_hits[], risk_score ∈ [0–1]6–10 s
`inline`Posts or previews comments0.5 s
`check`CI gate (`risk_score > threshold`)0.2 s
`generate_tests`Stub pytest files + open PR4–6 s

> Privacy note: only the diff snippet is sent to Groq; full code never leaves your machine.


3 Quick Start (5 min)

bash
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/6883

Store secrets once (env-var OR keyring):

python
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`


4 Architecture

pipeline
  • SQLite → diff cache (24 h)
  • ChromaDB → hunk embeddings
  • Back-off → GitHub retries (403/5xx)
  • Tracing → OpenTelemetry spans
  • Detailed diagram: `docs/ARCHITECTURE.md`

5 Benchmark

See `bench/benchmarks.md`:

10 popular OSS PRs → avg ⏱ 8.1 s analyze, 💰 \$0.0008 Groq cost, 96 % comment acceptance.


6 Docs


7 Day-by-Day Log

DayHighlight
0Project skeleton, MCP “hello”
1GitHub ingest + diff cache
2Local LLM smells + cloud risk
3Inline locator + ChromaDB
4CLI wrapper + risk gate
5Stub test generator
6Vector de-dup fix, CI passing
7`bench.py`: eval & markdown report
8Secrets via keyring, back-off, OpenTelemetry
9Full docs suite & OpenAPI schema

Full changelog: `docs/CHANGELOG.md`


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!

Frequently asked questions

What is codeview-mcp?

codeview-mcp is 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.

How do I install codeview-mcp?

Open the GitHub repository and follow its README. Most MCP servers are added to your client's MCP config, then called by your agent.

Is codeview-mcp open source?

Yes — it is hosted on GitHub at https://github.com/mann-uofg/codeview-mcp and has 2 stars.

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