Code Review Agent MCP
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Code Review Agent MCPReview src/auth.py for security issues"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Code Review Agent MCP
AI code review agent that doesn't sugarcoat. If your code is bad, it tells you. Bluntly.
An AI-powered MCP (Model Context Protocol) server that reviews your code the way experienced maintainers review patches on serious projects: technically, directly, without diplomacy.
What it does
AI code review — reviews code snippets, files, git diffs, and commits
Returns findings with severity labels:
CRITICAL,MAJOR,MINOR,NIT,CLEANCites specific line numbers for every finding
Explains why something is bad, then how to fix it
Ends with a clear verdict: "Ship it." or "Do not merge until..."
No false praise. No hedging. No apologies. No filler.
Related MCP server: greybeard
Why?
Most AI code reviewers are trained to be polite. They preface criticism with "great work but...", they hedge with "you might want to consider...", they soften findings with "this could potentially be an issue".
That's not how serious code review works. On real projects (Linux kernel, PostgreSQL, Redis, SQLite), maintainers review patches directly. They find problems and call them out clearly. They don't exist to make the author feel good — they exist to make the code better.
This MCP encodes that tradition. The personality IS the product.
Anti-RLHF engineering
LLMs are RLHF-trained to be polite. This AI agent fights that with three layers:
System prompt — 12 core principles (code-not-coder, verdict-first, severity labels, line citations, no false praise, no hedging, no apologies, no filler, second person, no demographic references, no refusal, no hallucinated bugs)
Post-processor — strips 40+ banned phrases (hedging, softeners, apologies, filler) from LLM output
Validator — checks output has severity labels, line citations, and verdict; flags vague problem statements as hallucination signals
Tools
Tool | Description |
| Review a code snippet |
| Review a file from disk (sandboxed) |
| Review a git diff |
| Review a git commit ( |
| List severity labels with definitions |
Harshness levels
Level | Behavior |
| Softens language slightly. Still reports all findings. |
| Default blunt review. Direct, technical, no diplomacy. |
| No softening. "This is wrong." not "This should be changed." |
| Maximum bluntness. Short sentences. Imperative voice. |
Installation
pip install code-review-agent-mcpOr with uv:
uv pip install code-review-agent-mcpConfiguration
Claude Desktop
Add to claude_desktop_config.json:
{
"mcpServers": {
"code-review-agent": {
"command": "python",
"args": ["-m", "blunt_codereview.server"]
}
}
}Or if installed via pip:
{
"mcpServers": {
"code-review-agent": {
"command": "blunt-codereview-mcp"
}
}
}Cursor
Add to .cursor/mcp.json:
{
"mcpServers": {
"code-review-agent": {
"command": "python",
"args": ["-m", "blunt_codereview.server"]
}
}
}Usage examples
Review a code snippet
User: Review this code for me
def get_user(username):
import sqlite3
conn = sqlite3.connect("users.db")
cursor = conn.cursor()
query = f"SELECT * FROM users WHERE username = '{username}'"
cursor.execute(query)
return cursor.fetchone()MCP response:
## Code Review: snippet
### Findings
**CRITICAL** `snippet:7` — SQL injection
The query uses an f-string with user input, allowing SQL injection. Use parameterized queries: `cursor.execute("SELECT * FROM users WHERE username = ?", (username,))`.
### Verdict
Do not merge until CRITICAL is fixed.Review a file
User: Review src/auth.py
MCP calls review_file with file_path="src/auth.py"
Returns blunt review with line citations.Review a commit
User: Review the last commit
MCP calls review_commit with commit_ref="HEAD"
Returns blunt review of the diff.Severity labels
Label | When to use |
CRITICAL | Security vulnerability, data loss, deadlock, RCE, anything that ships broken |
MAJOR | Logic error, race condition, resource leak, broken edge case, wrong abstraction |
MINOR | Style, naming, missing test, redundant code, brittle assumption |
NIT | Cosmetic, formatting, comment wording |
CLEAN | Explicitly state when a section is fine. Prevents invented-bug bias. |
Security
This MCP server implements security sandboxing:
File access is sandboxed to the current working directory by default
Sensitive paths (
.ssh,.aws,.env,/etc/passwd, etc.) are refusedGit refs are validated against a strict character whitelist to prevent option injection
Subprocess calls use
shell=Falseand disable global git config
See SECURITY.md for the full threat model.
Development
# Install in development mode
pip install -e ".[dev]"
# Run tests
pytest
# Run tests with coverage
pytest --cov=blunt_codereviewBenchmark snippets
The benchmarks/ directory contains 5 regression snippets that verify the reviewer:
SQL injection — expects CRITICAL, line citation, "Do not merge"
Mutable default — expects MAJOR
Clean code (binary search) — expects CLEAN, "Ship it" (anti-hallucination test)
Swallowed exception — expects MAJOR
Off-by-one — expects MAJOR
The clean code benchmark is the most important — it catches hallucination. If the reviewer invents bugs in correct code, the anti-RLHF system is broken.
License
MIT
Acknowledgments
This project encodes the kernel maintainer tradition of code review — a methodology practiced by many senior engineers across many projects (Linux kernel, PostgreSQL, Redis, SQLite, and others). We cite the tradition, not any single practitioner.
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