CodeView MCP 🪄
About
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.
Details
- Author
- mann-uofg
- GitHub stars
- 2
- Downloads
- 349
- Categories
- Developer Tools
Jump to
- Static regex rules for critical code smells
- Local LLM heuristics with no cloud cost
- Cloud LLM human-style summary and risk score (0–1)
- One-click inline comment accept or ignore
- SQLite diff cache and ChromaDB hunk embeddings
- OpenTelemetry tracing and GitHub back-off logic
Setting up with Highlight
This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:
- Download and install Highlight from highlightai.com/download
- Navigate to the plugins tab and select "Add Custom Plugin"
-
Configure the plugin with the settings below
Plugin Name
CodeView MCP 🪄Command (node, npx, python, etc.)Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
- Enable "Start Automatically" if you want the plugin to start when Highlight launches
From the repository
Clone the repository, create a Python virtual environment, install dependencies, and install the package with pip install -e .. Run a smoke test with reviewgenie/codeview ping https://github.com/psf/requests/pull/6883. Store secrets such as a GitHub PAT and Groq/OpenAI API key using the codeview_mcp.secret keyring module.
Claude Desktop / Cursor
Paste into your MCP client config file to install this server.
{
"mcpServers": {
"codeview mcp \ud83e\ude84": {
"codeview-mcp": {
"command": "python",
"args": [
"-m",
"venv",
".venv",
"&&",
"source",
".venv/bin/activate"
]
}
}
}
}McpServers
{
"codeview-mcp": {
"command": "python",
"args": [
"-m",
"venv",
".venv",
"&&",
"source",
".venv/bin/activate"
]
}
}CodeView MCP 🪄
_Powered by MCP, CodeLlama-13B (local), Llama-3.1-8b-instant (cloud)_
---
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
| 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.
---
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 .
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