from agenticx.tools import tool

@tool
def search_web(query: str) -> str:
    """Search the web for information.

    Args:
        query: The search query string.

    Returns:
        Search results as text.
    """
    # implementation
    return f"Results for: {query}"

@tool
def read_file(path: str) -> str:
    """Read contents of a local file."""
    with open(path) as f:
        return f.read()

from agenticx.core import BaseTool

class DatabaseQuery(BaseTool):
    name = "database_query"
    description = "Query the project database."

    def __init__(self, connection_string: str):
        super().__init__()
        self.conn = connect(connection_string)

    def _run(self, sql: str) -> str:
        return self.conn.execute(sql).fetchall()

from agenticx.core import ToolRegistry

registry = ToolRegistry()
registry.register(search_web)
registry.register(read_file)

# List all registered tools
for t in registry.list_tools():
    print(f"{t.name}: {t.description}")

from agenticx.protocols import MCPClient

client = MCPClient(server_url="http://localhost:3000")
tools = client.list_tools()

# Use MCP tools like local tools
result = client.call_tool("search", {"query": "AI agents"})

from agenticx.protocols import MCPServer

server = MCPServer(host="0.0.0.0", port=3000)
server.register_tool(search_web)
server.register_tool(read_file)
server.start()

from agenticx.tools.skill_bundle import SkillBundleLoader, SkillTool

loader = SkillBundleLoader()
skill_tool = SkillTool(loader=loader)
# Agents can invoke: skill_tool("list") or skill_tool("read <skill-name>")

@tool
def contextual_tool(query: str, _context: "ToolContext" = None) -> str:
    """A tool that uses execution context."""
    if _context:
        user = _context.user
        session = _context.session_id
    return f"Processed: {query}"