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+FastMCP-based MCP Server for Workshop Keyword Search Tool -This module implements a Model Context Protocol (MCP) server that exposes -the keyword search functionality as a tool for AI agents. +This module implements an MCP server using FastMCP that exposes the +keyword search functionality as a tool for AI agents over stdio. + +It preserves the original CLI entrypoint (workshop-mcp-server) while +simplifying protocol handling via FastMCP's high-level API. """ -import asyncio -import json -import logging -import sys -from typing import Any, Dict, List, Sequence - -from mcp.server import Server -from mcp.server.models import InitializationOptions -from mcp.server.stdio import stdio_server -from mcp.server.lowlevel import NotificationOptions -from mcp.types import ( - CallToolRequest, - CallToolResult, - ListToolsRequest, - ListToolsResult, - Tool, - TextContent, -) +from __future__ import annotations + +from typing import Annotated, Any + +from fastmcp import Context, FastMCP +from pydantic import Field from .keyword_search import KeywordSearchTool -# Configure logging -logging.basicConfig( - level=logging.INFO, - format='%(asctime)s - %(name)s - %(levelname)s - %(message)s', - handlers=[ - logging.StreamHandler(sys.stderr) - ] +# Instantiate FastMCP server +mcp = FastMCP( + name="workshop-mcp-server", ) -logger = logging.getLogger(__name__) +# Reuse a single instance of the tool +_keyword_search_tool = KeywordSearchTool() -class WorkshopMCPServer: - """ - MCP Server implementation for the Workshop Keyword Search Tool. - - This server exposes the keyword search functionality through the MCP protocol, - allowing AI agents to search for keywords across directory trees. - """ - - def __init__(self) -> None: - """Initialize the MCP server with keyword search tool.""" - self.server = Server("workshop-mcp-server") - self.keyword_search_tool = KeywordSearchTool() - self._setup_handlers() - - def _setup_handlers(self) -> None: - """Set up MCP protocol handlers.""" - - @self.server.list_tools() - async def handle_list_tools() -> List[Tool]: - """ - Handle list_tools request - return available tools with their schemas. - - Returns: - List of available tools with input schemas - """ - return [ - Tool( - name="keyword_search", - description=( - "Search for keyword occurrences across directory trees. " - "Supports multiple text file formats (.py, .java, .js, .ts, " - ".html, .css, .json, .xml, .md, .txt, .yml, .yaml, etc.) " - "and provides detailed statistics about matches." - ), - inputSchema={ - "type": "object", - "properties": { - "keyword": { - "type": "string", - "description": "The keyword to search for (case-sensitive)", - "minLength": 1 - }, - "root_paths": { - "type": "array", - "description": "List of directory paths to search in", - "items": { - "type": "string", - "description": "Directory path to search" - }, - "minItems": 1 - } - }, - "required": ["keyword", "root_paths"] - } - ) - ] - - @self.server.call_tool() - async def handle_call_tool(name: str, arguments: Dict[str, Any]) -> List[TextContent]: - """ - Handle call_tool request - execute the requested tool. - - Args: - name: Name of the tool to execute - arguments: Tool arguments - - Returns: - List of text content with tool results - - Raises: - ValueError: If tool name is unknown or arguments are invalid - """ - if name != "keyword_search": - raise ValueError(f"Unknown tool: {name}") - - try: - # Validate required arguments - if "keyword" not in arguments: - raise ValueError("Missing required argument: keyword") - - if "root_paths" not in arguments: - raise ValueError("Missing required argument: root_paths") - - keyword = arguments["keyword"] - root_paths = arguments["root_paths"] - - # Validate argument types - if not isinstance(keyword, str): - raise ValueError("keyword must be a string") - - if not isinstance(root_paths, list): - raise ValueError("root_paths must be a list") - - if not all(isinstance(path, str) for path in root_paths): - raise ValueError("All root_paths must be strings") - - # Execute keyword search - logger.info(f"Executing keyword search for '{keyword}' in {len(root_paths)} paths") - - result = await self.keyword_search_tool.execute(keyword, root_paths) - - # Format result as JSON - result_json = json.dumps(result, indent=2, ensure_ascii=False) - - logger.info( - f"Search completed successfully: " - f"{result['summary']['total_files_searched']} files searched, " - f"{result['summary']['total_occurrences']} occurrences found" - ) - - return [ - TextContent( - type="text", - text=result_json - ) - ] - - except Exception as e: - error_msg = f"Error executing keyword_search: {str(e)}" - logger.error(error_msg) - - # Return error as structured JSON - error_result = { - "error": { - "type": type(e).__name__, - "message": str(e), - "tool": "keyword_search", - "arguments": arguments - } - } - - return [ - TextContent( - type="text", - text=json.dumps(error_result, indent=2) - ) - ] - - async def run(self) -> None: - """ - Run the MCP server with stdio transport. - - This method starts the server and handles the MCP protocol communication - over stdin/stdout. - """ - logger.info("Starting Workshop MCP Server") - - try: - async with stdio_server() as (read_stream, write_stream): - await self.server.run( - read_stream, - write_stream, - InitializationOptions( - server_name="workshop-mcp-server", - server_version="0.1.0", - capabilities=self.server.get_capabilities( - notification_options=NotificationOptions(), - experimental_capabilities={} - ) - ) - ) - except KeyboardInterrupt: - logger.info("Server shutdown requested") - except Exception as e: - logger.error(f"Server error: {e}") - raise - - -async def main() -> None: - """ - Main entry point for the MCP server. - - Creates and runs the Workshop MCP Server instance. +@mcp.tool( + name="keyword_search", + description=( + "Search for keyword occurrences across directory trees. " + "Supports multiple text file formats (.py, .java, .js, .ts, " + ".html, .css, .json, .xml, .md, .txt, .yml, .yaml, etc.) " + "and provides detailed statistics about matches." + ), + annotations={ + "title": "Keyword Search", + "readOnlyHint": True, + "openWorldHint": True, + "idempotentHint": True, + }, +) +async def keyword_search( + keyword: Annotated[ + str, + Field(description="The keyword to search for (case-sensitive)", min_length=1), + ], + root_paths: Annotated[ + list[str], + Field(description="List of directory paths to search in", min_length=1), + ], + ctx: Context, +) -> dict[str, Any]: + """Execute an asynchronous keyword search across one or more root paths. + + Parameters are validated and documented via type annotations; FastMCP will + generate the input schema automatically. Returning a dict enables structured + output in addition to traditional content blocks. """ - server = WorkshopMCPServer() - await server.run() + await ctx.info( + f"Executing keyword search for '{keyword}' in {len(root_paths)} paths" + ) + + # Delegate to the existing async tool implementation + result = await _keyword_search_tool.execute(keyword, root_paths) + + # FastMCP automatically sends structured content for dict-like results + return result def sync_main() -> None: - """ - Synchronous entry point for the MCP server. - - This function is used as the script entry point in pyproject.toml. - """ - try: - asyncio.run(main()) - except KeyboardInterrupt: - logger.info("Server stopped by user") - sys.exit(0) - except Exception as e: - logger.error(f"Fatal error: {e}") - sys.exit(1) + """Synchronous entry point for the FastMCP server (stdio by default).""" + # FastMCP defaults to stdio transport, preserving original behavior + mcp.run() if __name__ == "__main__": - sync_main() \ No newline at end of file + sync_main()