Add GPT Researcher MCP server implementation

Introduce an MCP server for GPT Researcher using FastMCP, enabling AI assistants to perform web research and generate reports via SSE. Includes async processing steps with real-time updates and a runnable server script.

src/phoenix_technologies/test/__init__.py

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+"""
+GPT Researcher MCP Server
+
+This module provides an MCP server implementation for GPT Researcher,
+allowing AI assistants to perform web research and generate reports via the MCP protocol.
+"""
+
+__version__ = "0.1.0"

src/phoenix_technologies/test/server.py

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+"""
+GPT Researcher MCP Server
+
+This script implements an MCP server for GPT Researcher, allowing AI assistants
+to conduct web research and generate reports via the MCP protocol.
+"""
+
+import os
+import json
+from typing import Dict, Any, AsyncGenerator
+import asyncio
+from mcp.server.fastmcp import FastMCP
+
+# Initialize FastMCP server
+mcp = FastMCP("GPT Researcher", host="0.0.0.0", port=8000, timeout_keep_alive=720)
+
+@mcp.tool()
+async def updates(tool_input: Dict[str, Any]) -> AsyncGenerator[str, None]:
+    """
+    Ein MCP-Tool, das Zwischenupdates über SSE streamt.
+    tool_input ist ein Dictionary, das die Eingabeparameter des Tools enthält.
+    """
+    print(f"Tool gestartet mit Input: {tool_input}")
+
+    async def _process_step_1():
+        await asyncio.sleep(1)
+        return {"status": "Step 1 complete", "details": "Initial processing done."}
+
+    async def _process_step_2():
+        await asyncio.sleep(2)
+        return {"status": "Step 2 in progress", "progress": 50}
+
+    async def _process_step_3():
+        await asyncio.sleep(1)
+        return {"status": "Step 2 complete", "progress": 100, "intermediate_result": "Partial data available"}
+
+    async def _generate_final_output():
+        await asyncio.sleep(1)
+        return {"final_data": "All steps finished successfully.", "summary": tool_input.get("summary_needed", False)}
+
+    # Schritt 1
+    update1 = await _process_step_1()
+    yield f"event: tool_update\ndata: {json.dumps(update1)}\n\n"
+    print("Update 1 gesendet")
+
+    # Schritt 2
+    update2 = await _process_step_2()
+    yield f"event: tool_update\ndata: {json.dumps(update2)}\n\n"
+    print("Update 2 gesendet")
+
+    # Schritt 3
+    update3 = await _process_step_3()
+    yield f"event: tool_update\ndata: {json.dumps(update3)}\n\n"
+    print("Update 3 gesendet")
+
+    # Finale Ausgabe (kann auch als spezielles Event gesendet werden)
+    final_output = await _generate_final_output()
+    yield f"event: tool_result\ndata: {json.dumps(final_output)}\n\n"
+    print("Finales Ergebnis gesendet")
+
+    # Optional: Signal für Stream-Ende, falls vom Client benötigt oder von FastMCP erwartet
+    # yield "event: stream_end\ndata: {}\n\n"
+    print("Tool-Ausführung beendet.")
+
+def run_server():
+    """Run the MCP server using FastMCP's built-in event loop handling."""
+
+    # Add startup message
+    print("🚀 Test MCP Server starting... Check researcher_mcp_server.log for details")
+
+    # Let FastMCP handle the event loop
+    try:
+        mcp.run("sse")
+    except Exception as e:
+        print(f"❌ MCP Server error: {str(e)}")
+        return
+
+    print("✅ MCP Server stopped")
+
+
+if __name__ == "__main__":
+    # Use the non-async approach to avoid asyncio nesting issues
+    run_server()