test grade + mcp

app.py

@@ -1,122 +1,118 @@
+#!/usr/bin/env python3
+"""
+Sample MCP Server for ChatGPT Deep Research Integration
+
+This server implements the Model Context Protocol (MCP) with search and fetch
+capabilities designed to work with ChatGPT's deep research feature.
+"""
+
 import logging
+import os
 import sys
 from pathlib import Path
 from typing import Dict, List, Any
-import gradio as gr
-import threading
-import uvicorn
-
 sys.path.append(str(Path(__file__).parent / "src"))
 from src.IberleyAI.Tools.IberleyIAPrompt import IberleyIAPrompt
 from fastmcp import FastMCP
 
+# Configure logging
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)
 
-def create_mcp():
+server_instructions = """
+This MCP server provides search and document retrieval capabilities 
+for deep research. Use the search tool to find relevant documents 
+based on keywords, then use the fetch tool to retrieve complete 
+document content with citations.
+"""
+
+def create_server():
+    """Create and configure the MCP server with search and fetch tools."""
+
     mcp = FastMCP(name="IberleyIA MCP Server",
-                  instructions="Provides legal search and fetch tools.")
+                  instructions=server_instructions)
 
     @mcp.tool()
     async def search(query: str) -> Dict[str, List[Dict[str, Any]]]:
+        """
+        Search for legal information in the berleyAI chatbot connection
+        
+        This tool searches through the vector store to find semantically relevant matches.
+        Returns a list of search results with basic information. Use the fetch tool to get 
+        complete document content.
+        
+        Args:
+            query: Search query string. Natural language queries work best for semantic search.
+        
+        Returns:
+            Dictionary with 'content' key containing list of matching documents.
+            Each result includes type, text.
+        """
         if not query or not query.strip():
             return {"content": []}
+
         response = IberleyIAPrompt.iberley_ia_prompt(query)
-        return {"content": [{"type": "text", "text": response}]}
+
+        result = {
+            "content": [
+                {
+                "type": "text",
+                "text": response
+                }
+            ]
+        }
+        return result
 
     @mcp.tool()
     async def fetch(id: str) -> Dict[str, Any]:
+        """
+        Search for legal information in the berleyAI chatbot connection given an id for a
+        legal document withing a query in natural language
+        
+        This tool searches through the vector store to find semantically relevant matches.
+        Returns a list of search results with basic information. Use the fetch tool to get 
+        complete document content.
+        
+        Args:
+            query: Search query string. Natural language queries work best for semantic search.
+        
+        Returns:
+            Dictionary with 'content' key containing list of matching documents.
+            Each result includes type, text.
+        """
         if not id or not id.strip():
             return {"content": []}
+
         response = IberleyIAPrompt.iberley_ia_prompt(id)
-        return {"content": [{"type": "text", "text": response}]}
+
+        result = {
+            "content": [
+                {
+                "type": "text",
+                "text": response
+                }
+            ]
+        }
+        return result
 
     return mcp
 
 
 def main():
-    mcp = create_mcp()
-
-    def run_search(query):
-        return IberleyIAPrompt.iberley_ia_prompt(query)
-
-    def run_fetch(doc_id):
-        return IberleyIAPrompt.iberley_ia_prompt(doc_id)
-
-    with gr.Blocks() as demo:
-        gr.Markdown("## IberleyIA - Manual Testing UI")
+    """Main function to start the MCP server."""
+    #conection string should be "https://iberley-iberleyia.hf.space/sse"
+    server = create_server()
 
-        with gr.Tab("Search"):
-            query = gr.Textbox(label="Query")
-            output = gr.Textbox(label="Response")
-            gr.Button("Run Search").click(fn=run_search, inputs=query, outputs=output)
+    logger.info("Starting MCP server on 0.0.0.0:7860")
+    logger.info("Server will be accessible via SSE transport")
 
-        with gr.Tab("Fetch"):
-            doc_id = gr.Textbox(label="Document ID")
-            output2 = gr.Textbox(label="Response")
-            gr.Button("Run Fetch").click(fn=run_fetch, inputs=doc_id, outputs=output2)
-
-    # 1) Lanza Gradio en 7860 (único puerto permitido en HF Spaces)
-    app, _, _ = demo.launch(
-        server_name="0.0.0.0",
-        server_port=7860,
-        share=False,
-        prevent_thread_lock=True,
-    )
-
-    # 2) Monta el endpoint MCP en /sse
-    try:
-        sse_app = mcp.create_app(transport="sse")
-        app.mount("/sse", sse_app)
-        logger.info("Mounted MCP SSE endpoint at /sse")
-    except AttributeError:
-        logger.error("Tu versión de fastmcp no tiene create_app(), tendrás que actualizar fastmcp.")
-
-    mcp = create_mcp()
-
-    def run_search(query):
-        return IberleyIAPrompt.iberley_ia_prompt(query)
-
-    def run_fetch(doc_id):
-        return IberleyIAPrompt.iberley_ia_prompt(doc_id)
-
-    with gr.Blocks() as demo:
-        gr.Markdown("## IberleyIA - Manual Testing UI")
-
-        with gr.Tab("Search"):
-            query = gr.Textbox(label="Query")
-            output = gr.Textbox(label="Response")
-            gr.Button("Run Search").click(fn=run_search, inputs=query, outputs=output)
-
-        with gr.Tab("Fetch"):
-            doc_id = gr.Textbox(label="Document ID")
-            output2 = gr.Textbox(label="Response")
-            gr.Button("Run Fetch").click(fn=run_fetch, inputs=doc_id, outputs=output2)
-
-    # 1) Gradio corre en 7860
-    app, _, _ = demo.launch(
-        server_name="0.0.0.0",
-        server_port=7860,
-        share=False,
-        prevent_thread_lock=True,
-    )
-
-    # 2) MCP corre en /sse dentro del mismo servidor uvicorn
-    # Si FastMCP soporta create_app():
     try:
-        sse_app = mcp.create_app(transport="sse")
-        app.mount("/sse", sse_app)
-        logger.info("Mounted MCP SSE endpoint at /sse")
-    except AttributeError:
-        logger.error("Tu versión de fastmcp no soporta create_app(). Usa mcp.run en otro hilo.")
-        # fallback: lanzar MCP en thread separado (aunque intentará usar el mismo puerto)
-        threading.Thread(
-            target=lambda: mcp.run(transport="sse", host="0.0.0.0", port=7860, path="/sse"),
-            daemon=True
-        ).start()
-
-    # 3) Mantener vivo uvicorn
-    uvicorn.run(app, host="0.0.0.0", port=7860)
+        server.run(transport="sse", host="0.0.0.0", port=7860)
+    except KeyboardInterrupt:
+        logger.info("Server stopped by user")
+    except Exception as e:
+        logger.error(f"Server error: {e}")
+        raise
 
 
 if __name__ == "__main__":