"""Search input/output models for Community Research MCP."""
from typing import Optional
from pydantic import BaseModel, ConfigDict, Field, field_validator
from models.config import ResponseFormat
class CommunitySearchInput(BaseModel):
"""Input model for community search."""
model_config = ConfigDict(
str_strip_whitespace=True, validate_assignment=True, extra="forbid"
)
language: str = Field(
...,
description="Programming language (e.g., 'Python', 'JavaScript', 'Rust')",
min_length=2,
max_length=50,
)
topic: str = Field(
...,
description=(
"Specific, detailed topic. Be VERY specific - not just 'settings' or 'performance'. "
"Examples: 'FastAPI background task queue with Redis', "
"'React custom hooks for form validation', "
"'Docker multi-stage builds to reduce image size'"
),
min_length=10,
max_length=500,
)
goal: Optional[str] = Field(
default=None,
description="What you want to achieve (e.g., 'async task processing without blocking requests')",
max_length=500,
)
current_setup: Optional[str] = Field(
default=None,
description=(
"Your current tech stack and setup. HIGHLY RECOMMENDED for implementation questions. "
"Example: 'FastAPI app with SQLAlchemy, need queue for long-running jobs'"
),
max_length=1000,
)
response_format: ResponseFormat = Field(
default=ResponseFormat.MARKDOWN,
description="Output format: 'markdown' (default, human-readable) or 'json' (machine-readable)",
)
use_planning: bool = Field(
default=False,
description="Enable research planning mode for complex queries. Uses AI to create a structured research strategy before executing the search.",
)
thinking_mode: str = Field(
default="balanced",
description="Analysis depth mode: 'quick' (fast, basic), 'balanced' (default), or 'deep' (thorough, slower)",
)
expanded_mode: bool = Field(
default=False,
description="If true, allow expanded result budgets per source (higher caps, still read-only).",
)
use_fixtures: bool = Field(
default=False,
description="If true, return deterministic fixture data without hitting live sources (for health checks).",
)
@field_validator("topic")
@classmethod
def validate_topic_specificity(cls, v: str) -> str:
"""Ensure topic is specific enough to get useful results."""
v = v.strip()
# List of vague terms that indicate a non-specific query
vague_terms = [
"settings",
"configuration",
"config",
"setup",
"performance",
"optimization",
"best practices",
"how to",
"tutorial",
"getting started",
"basics",
"help",
"issue",
"problem",
"error",
"debugging",
"install",
"installation",
]
# Check if topic is just one or two vague words
words = v.lower().split()
if len(words) <= 2 and any(term in v.lower() for term in vague_terms):
raise ValueError(
f"Topic '{v}' is too vague. Be more specific! "
f"Instead of 'settings', say 'GUI settings dialog with tabs and save/load buttons'. "
f"Instead of 'performance', say 'reduce Docker image size with multi-stage builds'. "
f"Include specific technologies, libraries, or patterns you're interested in."
)
return v
class DeepAnalyzeInput(BaseModel):
"""Input model for deep workspace analysis."""
model_config = ConfigDict(
str_strip_whitespace=True, validate_assignment=True, extra="forbid"
)
user_query: str = Field(
...,
description="What you want to understand or improve about your codebase",
min_length=10,
max_length=1000,
)
workspace_path: Optional[str] = Field(
default=None,
description="Path to workspace to analyze (defaults to current directory)",
)
target_language: Optional[str] = Field(
default=None,
description="Specific language to focus on (e.g., 'Python', 'JavaScript')",
)
