refactor: deduplicate tool cache marker helper in base provider

nanobot/providers/anthropic_provider.py

@@ -250,36 +250,6 @@ class AnthropicProvider(LLMProvider):
     # Prompt caching
     # ------------------------------------------------------------------
 
-    @staticmethod
-    def _tool_name(tool: dict[str, Any]) -> str:
-        name = tool.get("name")
-        if isinstance(name, str):
-            return name
-        fn = tool.get("function")
-        if isinstance(fn, dict):
-            fname = fn.get("name")
-            if isinstance(fname, str):
-                return fname
-        return ""
-
-    @classmethod
-    def _tool_cache_marker_indices(cls, tools: list[dict[str, Any]]) -> list[int]:
-        if not tools:
-            return []
-
-        tail_idx = len(tools) - 1
-        last_builtin_idx: int | None = None
-        for i in range(tail_idx, -1, -1):
-            if not cls._tool_name(tools[i]).startswith("mcp_"):
-                last_builtin_idx = i
-                break
-
-        ordered_unique: list[int] = []
-        for idx in (last_builtin_idx, tail_idx):
-            if idx is not None and idx not in ordered_unique:
-                ordered_unique.append(idx)
-        return ordered_unique
-
     @classmethod
     def _apply_cache_control(
         cls,

nanobot/providers/base.py

@@ -48,7 +48,7 @@ class LLMResponse:
     usage: dict[str, int] = field(default_factory=dict)
     reasoning_content: str | None = None  # Kimi, DeepSeek-R1 etc.
     thinking_blocks: list[dict] | None = None  # Anthropic extended thinking
-    
+
     @property
     def has_tool_calls(self) -> bool:
         """Check if response contains tool calls."""
@@ -73,7 +73,7 @@ class GenerationSettings:
 class LLMProvider(ABC):
     """
     Abstract base class for LLM providers.
-    
+
     Implementations should handle the specifics of each provider's API
     while maintaining a consistent interface.
     """
@@ -150,6 +150,38 @@ class LLMProvider(ABC):
             result.append(msg)
         return result
 
+    @staticmethod
+    def _tool_name(tool: dict[str, Any]) -> str:
+        """Extract tool name from either OpenAI or Anthropic-style tool schemas."""
+        name = tool.get("name")
+        if isinstance(name, str):
+            return name
+        fn = tool.get("function")
+        if isinstance(fn, dict):
+            fname = fn.get("name")
+            if isinstance(fname, str):
+                return fname
+        return ""
+
+    @classmethod
+    def _tool_cache_marker_indices(cls, tools: list[dict[str, Any]]) -> list[int]:
+        """Return cache marker indices: builtin/MCP boundary and tail index."""
+        if not tools:
+            return []
+
+        tail_idx = len(tools) - 1
+        last_builtin_idx: int | None = None
+        for i in range(tail_idx, -1, -1):
+            if not cls._tool_name(tools[i]).startswith("mcp_"):
+                last_builtin_idx = i
+                break
+
+        ordered_unique: list[int] = []
+        for idx in (last_builtin_idx, tail_idx):
+            if idx is not None and idx not in ordered_unique:
+                ordered_unique.append(idx)
+        return ordered_unique
+
     @staticmethod
     def _sanitize_request_messages(
         messages: list[dict[str, Any]],
@@ -177,7 +209,7 @@ class LLMProvider(ABC):
     ) -> LLMResponse:
         """
         Send a chat completion request.
-        
+
         Args:
             messages: List of message dicts with 'role' and 'content'.
             tools: Optional list of tool definitions.
@@ -185,7 +217,7 @@ class LLMProvider(ABC):
             max_tokens: Maximum tokens in response.
             temperature: Sampling temperature.
             tool_choice: Tool selection strategy ("auto", "required", or specific tool dict).
-        
+
         Returns:
             LLMResponse with content and/or tool calls.
         """

nanobot/providers/openai_compat_provider.py

@@ -151,34 +151,6 @@ class OpenAICompatProvider(LLMProvider):
             resolved = env_val.replace("{api_key}", api_key).replace("{api_base}", effective_base)
             os.environ.setdefault(env_name, resolved)
 
-    @staticmethod
-    def _tool_name(tool: dict[str, Any]) -> str:
-        fn = tool.get("function")
-        if isinstance(fn, dict):
-            name = fn.get("name")
-            if isinstance(name, str):
-                return name
-        name = tool.get("name")
-        return name if isinstance(name, str) else ""
-
-    @classmethod
-    def _tool_cache_marker_indices(cls, tools: list[dict[str, Any]]) -> list[int]:
-        if not tools:
-            return []
-
-        tail_idx = len(tools) - 1
-        last_builtin_idx: int | None = None
-        for i in range(tail_idx, -1, -1):
-            if not cls._tool_name(tools[i]).startswith("mcp_"):
-                last_builtin_idx = i
-                break
-
-        ordered_unique: list[int] = []
-        for idx in (last_builtin_idx, tail_idx):
-            if idx is not None and idx not in ordered_unique:
-                ordered_unique.append(idx)
-        return ordered_unique
-
     @classmethod
     def _apply_cache_control(
         cls,