semantic-complexity 0.0.24

Multi-dimensional code complexity analyzer

semantic-complexity (Python)

Multi-dimensional code complexity analyzer for Python.

Installation

pip install semantic-complexity

uv를 통한 설치

# 글로벌 도구로 설치
uv tool install semantic-complexity

# 또는 일회성 실행
uvx semantic-complexity path/to/file.py

MCP 서버 설치

Claude Code에서 MCP 서버로 사용하려면:

# MCP 서버 추가 (Python 버전)
claude mcp add sc-py "uvx --from semantic-complexity semantic-complexity-py-mcp"

기존 MCP 업데이트/재설치

# 1. 기존 MCP 삭제
claude mcp remove sc-py

# 2. 캐시 정리 (최신 버전 반영)
uv cache clean semantic-complexity --force

# 3. 재설치
claude mcp add sc-py "uvx --from semantic-complexity semantic-complexity-py-mcp"

# 설치 확인
claude mcp list

Usage

CLI

# Analyze a file
semantic-complexity path/to/file.py

# Analyze a directory
semantic-complexity path/to/project/

# Output as Markdown report
semantic-complexity path/to/project/ -f markdown -o report.md

# Filter by threshold
semantic-complexity path/to/project/ --threshold 20

Python API

from semantic_complexity import analyze_source, analyze_functions

# Analyze source code
result = analyze_source("""
def complex_function(x):
    if x > 0:
        for i in range(x):
            if i % 2 == 0:
                print(i)
""")

print(f"Weighted complexity: {result.weighted}")
print(f"Control: {result.control}")
print(f"Nesting: {result.nesting}")
print(f"Contributions: {result.contributions}")

# Analyze functions individually
functions = analyze_functions(source)
for fn in functions:
    print(f"{fn.name}: McCabe={fn.cyclomatic}, Dimensional={fn.dimensional.weighted}")

Domains

Domain	Weight	Description
Control (C)	1.0	Cyclomatic complexity (branches, loops)
Nesting (N)	1.5	Depth penalty
State (S)	2.0	State mutations and transitions
Async (A)	2.5	Async/await, coroutines
Coupling (Λ)	3.0	Hidden dependencies, side effects

License

MIT