diff --git a/src/phoenix_technologies/test/__init__.py b/src/phoenix_technologies/test/__init__.py
new file mode 100644
index 0000000..f372bcb
--- /dev/null
+++ b/src/phoenix_technologies/test/__init__.py
@@ -0,0 +1,8 @@
+"""
+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"
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diff --git a/src/phoenix_technologies/test/server.py b/src/phoenix_technologies/test/server.py
new file mode 100644
index 0000000..438d42e
--- /dev/null
+++ b/src/phoenix_technologies/test/server.py
@@ -0,0 +1,83 @@
+"""
+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()
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