fix lint

Author: LarFii

__init__.py

@@ -5,7 +5,7 @@
 ⚡ Transform research papers into working code automatically
 """
 
-__version__ = "1.0.2"
+__version__ = "1.0.3"
 __author__ = "DeepCode Team"
 __url__ = "https://github.com/HKUDS/DeepCode"
 

tools/bocha_search_server.py

@@ -1,4 +1,3 @@
-import asyncio
 import os
 import sys
 import json
@@ -15,13 +14,13 @@
     "bocha-search-mcp",
     prompt="""
 # Bocha Search MCP Server
-                 
+
 Bocha is a Chinese search engine for AI, This server provides tools for searching the web using Bocha Search API.
 It allows you to get enhanced search details from billions of web documents, including weather, news, wikis, healthcare, train tickets, images, and more.
 
 ## Available Tools
-                 
-### 1. bocha_web_search 
+
+### 1. bocha_web_search
 Search with Bocha Web Search and get enhanced search details from billions of web documents, including page titles, urls, summaries, site names, site icons, publication dates, image links, and more.
 
 ### 2. bocha_ai_search
@@ -36,7 +35,7 @@
 - Bocha AI search: Title, URL, Description, Published date, Site name, and structured data card
 
 If the API key is missing or invalid, appropriate error messages will be returned.
-"""
+""",
 )
 
 
@@ -69,7 +68,7 @@ async def bocha_web_search(
             "query": query,
             "summary": True,
             "freshness": freshness,
-            "count": count
+            "count": count,
         }
 
         headers = {
@@ -86,7 +85,7 @@ async def bocha_web_search(
             resp = response.json()
             if "data" not in resp:
                 return "Search error."
-            
+
             data = resp["data"]
 
             if "webPages" not in data:
@@ -142,7 +141,7 @@ async def bocha_ai_search(
             "freshness": freshness,
             "count": count,
             "answer": False,
-            "stream": False
+            "stream": False,
         }
 
         headers = {
@@ -163,9 +162,9 @@ async def bocha_ai_search(
                     content = {}
                     try:
                         content = json.loads(message["content"])
-                    except:
+                    except (json.JSONDecodeError, TypeError):
                         content = {}
-                        
+
                     # 网页
                     if message["content_type"] == "webpage":
                         if "value" in content:
@@ -177,12 +176,15 @@ async def bocha_ai_search(
                                     f"Published date: {item['datePublished']}\n"
                                     f"Site name: {item['siteName']}"
                                 )
-                    elif message["content_type"] != "image" and message["content"] != "{}":
+                    elif (
+                        message["content_type"] != "image"
+                        and message["content"] != "{}"
+                    ):
                         results.append(message["content"])
 
             if not results:
                 return "No results found."
-            
+
             return "\n\n".join(results)
 
     except httpx.HTTPStatusError as e:
@@ -203,8 +205,7 @@ def main():
             file=sys.stderr,
         )
         print(
-            "Get a Bocha API key from: "
-            "https://open.bochaai.com",
+            "Get a Bocha API key from: " "https://open.bochaai.com",
             file=sys.stderr,
         )
         sys.exit(1)
@@ -215,4 +216,4 @@ def main():
 
 
 if __name__ == "__main__":
-    main() 
+    main()

workflows/agent_orchestration_engine.py

@@ -113,7 +113,7 @@ def get_default_search_server(config_path: str = "mcp_agent.config.yaml"):
         if os.path.exists(config_path):
             with open(config_path, "r", encoding="utf-8") as f:
                 config = yaml.safe_load(f)
-            
+
             default_server = config.get("default_search_server", "brave")
             print(f"🔍 Using search server: {default_server}")
             return default_server
@@ -126,7 +126,9 @@ def get_default_search_server(config_path: str = "mcp_agent.config.yaml"):
         return "brave"
 
 
-def get_search_server_names(additional_servers: Optional[List[str]] = None) -> List[str]:
+def get_search_server_names(
+    additional_servers: Optional[List[str]] = None,
+) -> List[str]:
     """
     Get server names list with the configured default search server.
 
@@ -138,13 +140,13 @@ def get_search_server_names(additional_servers: Optional[List[str]] = None) -> L
     """
     default_search = get_default_search_server()
     server_names = [default_search]
-    
+
     if additional_servers:
         # Add additional servers, avoiding duplicates
         for server in additional_servers:
             if server not in server_names:
                 server_names.append(server)
-    
+
     return server_names
 
 
@@ -486,7 +488,7 @@ async def paper_reference_analyzer(paper_dir: str, logger) -> str:
 Focus on:
 1. **References section analysis** - Extract all citations from the References/Bibliography part
 2. References with high-quality GitHub implementations
-3. Papers cited for methodology, algorithms, or core techniques  
+3. Papers cited for methodology, algorithms, or core techniques
 4. Related work that shares similar technical approaches
 5. Implementation references that could provide code patterns
 
@@ -628,9 +630,7 @@ async def orchestrate_reference_intelligence_agent(
             return f.read()
 
     # Execute reference analysis
-    reference_result = await paper_reference_analyzer(
-        dir_info["paper_dir"], logger
-    )
+    reference_result = await paper_reference_analyzer(dir_info["paper_dir"], logger)
 
     # Save reference analysis result
     with open(reference_path, "w", encoding="utf-8") as f: