"""Tool for analyzing code for smells and suggesting refactorings."""
from __future__ import annotations
from typing import Any
from pydantic import BaseModel, Field, ConfigDict
from mcp_refactoring.adapters import get_adapter
from mcp_refactoring.utils import encode_toon, detect_language
class AnalyzeCodeInput(BaseModel):
"""Input for analyzing code."""
model_config = ConfigDict(str_strip_whitespace=True)
path: str = Field(
...,
description="File or directory path to analyze (e.g., 'src/order.py')",
min_length=1,
)
smells: list[str] | None = Field(
default=None,
description="Optional list of smell types to check for (e.g., ['long-method', 'large-class'])",
)
async def analyze_code(params: AnalyzeCodeInput) -> str:
"""Analyze code for smells and suggest refactorings.
Detects code smells like long methods, large classes, feature envy,
and suggests appropriate refactorings to address them.
Note: This feature requires backend support. Returns backend_supported: false
for backends that don't implement analysis yet.
Args:
params: Input parameters including path and optional smell filter
Returns:
TOON-formatted string with analysis results
"""
# Detect language from path
language = detect_language(params.path)
if language is None:
return encode_toon({
"status": "error",
"error": f"Could not detect language from path: {params.path}",
"hint": "Ensure the path has a recognized file extension (.py, .rb, .java, .go)",
})
# Get adapter for the language
adapter = get_adapter(language)
if adapter is None:
return encode_toon({
"status": "error",
"error": f"No backend available for language: {language}",
"hint": f"Install the {language} refactoring backend",
})
# Check if analysis is supported
if not adapter.supports_analysis():
return encode_toon({
"analysis": {
"path": params.path,
"language": language,
"backend_supported": False,
},
"smells": [],
"note": "Code smell analysis is not yet implemented for this backend. "
"This feature will be available in a future release.",
})
# Run analysis
result = await adapter.analyze_code(
path=params.path,
smells=params.smells,
)
# Format response
response: dict[str, Any] = {
"analysis": {
"path": result.path,
"language": result.language,
"backend_supported": result.backend_supported,
}
}
if result.smells:
response["smells"] = [
{
"type": s.smell_type,
"location": s.location,
"severity": s.severity.value,
"suggested_refactoring": s.suggested_refactoring,
"details": s.details,
}
for s in result.smells
]
else:
response["smells"] = []
return encode_toon(response)
