Add "High Value" Ruff Rules (#2255)

* Safe Fixes from ruff

* Fix remaining issues

* lint/check

* Fix mysterious ty check errors

* small cleanup

* pr fixes

examples/atproto_mcp/src/atproto_mcp/_atproto/__init__.py

@@ -7,14 +7,14 @@ from ._read import fetch_notifications, fetch_timeline, search_for_posts
 from ._social import follow_user_by_handle, like_post_by_uri, repost_by_uri
 
 __all__ = [
-    "get_client",
-    "get_profile_info",
     "create_post",
     "create_thread",
-    "fetch_timeline",
-    "search_for_posts",
     "fetch_notifications",
+    "fetch_timeline",
     "follow_user_by_handle",
+    "get_client",
+    "get_profile_info",
     "like_post_by_uri",
     "repost_by_uri",
+    "search_for_posts",
 ]

examples/get_file.py

@@ -1,3 +1,9 @@
+# /// script
+# dependencies = ["aiohttp", "fastmcp"]
+# ///
+
+# uv pip install aiohttp fastmcp
+
 import aiohttp
 
 from fastmcp.server import FastMCP

examples/memory.py

@@ -19,7 +19,7 @@ from typing import Annotated, Any, Self
 import asyncpg
 import numpy as np
 from openai import AsyncOpenAI
-from pgvector.asyncpg import register_vector  # Import register_vector
+from pgvector.asyncpg import register_vector
 from pydantic import BaseModel, Field
 from pydantic_ai import Agent
 
@@ -149,7 +149,9 @@ class MemoryNode(BaseModel):
         )
         self.importance += other.importance
         self.access_count += other.access_count
-        self.embedding = [(a + b) / 2 for a, b in zip(self.embedding, other.embedding)]
+        self.embedding = [
+            (a + b) / 2 for a, b in zip(self.embedding, other.embedding, strict=True)
+        ]
         self.summary = await do_ai(
             self.content, "Summarize the following text concisely.", str, deps
         )
@@ -281,9 +283,9 @@ async def display_memory_tree(deps: Deps) -> str:
 
 @mcp.tool
 async def remember(
-    contents: list[str] = Field(
-        description="List of observations or memories to store"
-    ),
+    contents: Annotated[
+        list[str], Field(description="List of observations or memories to store")
+    ],
 ):
     deps = Deps(openai=AsyncOpenAI(), pool=await get_db_pool())
     try: