"""Command: search - Search for skills using natural language query."""
from __future__ import annotations
import click
from rich.progress import Progress, SpinnerColumn, TextColumn
from rich.table import Table
from mcp_skills.cli.shared.console import console
from mcp_skills.models.config import MCPSkillsConfig
from mcp_skills.services.indexing.engine import IndexingEngine
from mcp_skills.services.skill_manager import SkillManager
@click.command()
@click.argument("query")
@click.option("--limit", type=int, default=10, help="Maximum results")
@click.option("--category", type=str, help="Filter by category")
@click.option(
"--search-mode",
type=click.Choice(
["semantic_focused", "graph_focused", "balanced", "current"],
case_sensitive=False,
),
help="Hybrid search weighting preset (overrides config file)",
)
def search(
query: str, limit: int, category: str | None, search_mode: str | None
) -> None:
"""Search for skills using natural language query.
Uses hybrid search combining vector embeddings (semantic similarity)
and knowledge graph (relationship similarity) to find relevant skills.
Search Modes:
- semantic_focused: Optimize for semantic similarity (90% vector, 10% graph)
- graph_focused: Optimize for relationships (30% vector, 70% graph)
- balanced: Equal weighting (50% vector, 50% graph)
- current: Default optimized preset (70% vector, 30% graph)
If --search-mode is not specified, loads from config.yaml or uses default.
Examples:
mcp-skillset search "testing skills for Python"
mcp-skillset search "fastapi" --category testing
mcp-skillset search "pytest" --limit 20 --search-mode semantic_focused
"""
console.print(f"🔍 [bold]Searching for:[/bold] {query}")
if category:
console.print(f"📁 [dim]Category filter: {category}[/dim]")
if search_mode:
console.print(f"⚖️ [dim]Search mode: {search_mode}[/dim]")
console.print()
try:
# Load config and optionally override with CLI flag
config = MCPSkillsConfig()
if search_mode:
# Override config with CLI flag
config.hybrid_search = config._get_preset(search_mode)
console.print(
f"[dim]Using {search_mode} preset: "
f"vector={config.hybrid_search.vector_weight:.1f}, "
f"graph={config.hybrid_search.graph_weight:.1f}[/dim]\n"
)
# Initialize services and perform search with progress indicator
with Progress(
SpinnerColumn(),
TextColumn("[progress.description]{task.description}"),
console=console,
transient=True, # Remove spinner after completion
) as progress:
task = progress.add_task("Loading skills index...", total=None)
# Initialize services with config
skill_manager = SkillManager()
indexing_engine = IndexingEngine(skill_manager=skill_manager, config=config)
progress.update(task, description="Searching...")
# Perform search
results = indexing_engine.search(query, category=category, top_k=limit)
progress.update(task, completed=True)
if not results:
console.print("[yellow]No results found[/yellow]")
console.print("\nTry:")
console.print(" • Using different keywords")
console.print(" • Removing category filter")
console.print(" • Running: mcp-skillset index --force")
return
# Display results in table
table = Table(title=f"Search Results ({len(results)} found)")
table.add_column("Name", style="cyan", no_wrap=True)
table.add_column("Category", style="magenta")
table.add_column("Score", justify="right", style="green")
table.add_column("Tags", style="dim")
for result in results:
skill = result.skill
score_str = f"{result.score:.2f}"
tags_str = ", ".join(skill.tags[:3]) # Show first 3 tags
if len(skill.tags) > 3:
tags_str += f" +{len(skill.tags) - 3}"
table.add_row(
skill.name,
skill.category,
score_str,
tags_str,
)
console.print(table)
console.print("\n[dim]Use 'mcp-skillset info <skill-id>' for details[/dim]")
except Exception as e:
console.print(f"[red]Search failed: {e}[/red]")
import logging
logger = logging.getLogger(__name__)
logger.exception("Search failed")
raise SystemExit(1)
