fix: https://github.com/BeehiveInnovations/zen-mcp-server/issues/251 added handling for safety_feedback from Gemini. FinishReason.STOP can be a hidden safety block from gemini or issued when it chooses not to respond.
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Fahad
@@ -231,6 +231,46 @@ class GeminiModelProvider(ModelProvider):
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# Extract usage information if available
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usage = self._extract_usage(response)
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# Intelligently determine finish reason and safety blocks
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finish_reason_str = "UNKNOWN"
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is_blocked_by_safety = False
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safety_feedback_details = None
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if response.candidates:
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candidate = response.candidates[0]
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finish_reason_enum = getattr(candidate, "finish_reason", None)
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if finish_reason_enum:
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# Handle both enum objects and string values
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finish_reason_str = getattr(finish_reason_enum, "name", str(finish_reason_enum))
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else:
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finish_reason_str = "STOP"
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# If content is empty, check safety ratings for the definitive cause
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if not response.text and hasattr(candidate, "safety_ratings"):
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for rating in candidate.safety_ratings:
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if getattr(rating, "blocked", False):
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is_blocked_by_safety = True
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# Provide details for logging/debugging
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category_name = (
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getattr(rating.category, "name", "UNKNOWN")
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if hasattr(rating, "category")
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else "UNKNOWN"
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)
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probability_name = (
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getattr(rating.probability, "name", "UNKNOWN")
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if hasattr(rating, "probability")
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else "UNKNOWN"
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)
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safety_feedback_details = f"Category: {category_name}, Probability: {probability_name}"
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break
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# Also check for prompt-level blocking (request rejected entirely)
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elif hasattr(response, "prompt_feedback") and getattr(response.prompt_feedback, "block_reason", None):
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is_blocked_by_safety = True
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finish_reason_str = "SAFETY" # This is a clear safety block
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block_reason_name = getattr(response.prompt_feedback.block_reason, "name", "UNKNOWN")
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safety_feedback_details = f"Prompt blocked, reason: {block_reason_name}"
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return ModelResponse(
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content=response.text,
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usage=usage,
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@@ -239,9 +279,9 @@ class GeminiModelProvider(ModelProvider):
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provider=ProviderType.GOOGLE,
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metadata={
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"thinking_mode": thinking_mode if capabilities.supports_extended_thinking else None,
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"finish_reason": (
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getattr(response.candidates[0], "finish_reason", "STOP") if response.candidates else "STOP"
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),
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"finish_reason": finish_reason_str,
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"is_blocked_by_safety": is_blocked_by_safety,
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"safety_feedback": safety_feedback_details,
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},
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)
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@@ -457,13 +457,101 @@ class SimpleTool(BaseTool):
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else:
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# Handle cases where the model couldn't generate a response
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finish_reason = model_response.metadata.get("finish_reason", "Unknown")
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logger.warning(f"Response blocked or incomplete for {self.get_name()}. Finish reason: {finish_reason}")
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tool_output = ToolOutput(
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status="error",
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content=f"Response blocked or incomplete. Finish reason: {finish_reason}",
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content_type="text",
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)
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metadata = model_response.metadata or {}
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finish_reason = metadata.get("finish_reason", "Unknown")
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if metadata.get("is_blocked_by_safety"):
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# Specific handling for content safety blocks
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safety_details = metadata.get("safety_feedback") or "details not provided"
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logger.warning(
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f"Response blocked by content safety policy for {self.get_name()}. "
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f"Reason: {finish_reason}, Details: {safety_details}"
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)
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tool_output = ToolOutput(
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status="error",
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content="Your request was blocked by the content safety policy. "
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"Please try modifying your prompt.",
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content_type="text",
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)
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else:
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# Handle other empty responses - could be legitimate completion or unclear blocking
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if finish_reason == "STOP":
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# Model completed normally but returned empty content - retry with clarification
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logger.info(
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f"Model completed with empty response for {self.get_name()}, retrying with clarification"
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)
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# Retry the same request with modified prompt asking for explicit response
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original_prompt = prompt
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retry_prompt = f"{original_prompt}\n\nIMPORTANT: Please provide a substantive response. If you cannot respond to the above request, please explain why and suggest alternatives."
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try:
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retry_response = provider.generate_content(
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prompt=retry_prompt,
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model_name=self._current_model_name,
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system_prompt=system_prompt,
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temperature=temperature,
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thinking_mode=(
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thinking_mode if provider.supports_thinking_mode(self._current_model_name) else None
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),
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images=images if images else None,
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)
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if retry_response.content:
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# Successful retry - use the retry response
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logger.info(f"Retry successful for {self.get_name()}")
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raw_text = retry_response.content
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# Update model info for the successful retry
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model_info = {
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"provider": provider,
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"model_name": self._current_model_name,
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"model_response": retry_response,
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}
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# Parse the retry response
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tool_output = self._parse_response(raw_text, request, model_info)
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logger.info(f"✅ {self.get_name()} tool completed successfully after retry")
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else:
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# Retry also failed - inspect metadata to find out why
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retry_metadata = retry_response.metadata or {}
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if retry_metadata.get("is_blocked_by_safety"):
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# The retry was blocked by safety filters
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safety_details = retry_metadata.get("safety_feedback") or "details not provided"
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logger.warning(
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f"Retry for {self.get_name()} was blocked by content safety policy. "
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f"Details: {safety_details}"
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)
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tool_output = ToolOutput(
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status="error",
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content="Your request was also blocked by the content safety policy after a retry. "
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"Please try rephrasing your prompt significantly.",
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content_type="text",
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)
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else:
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# Retry failed for other reasons (e.g., another STOP)
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tool_output = ToolOutput(
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status="error",
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content="The model repeatedly returned empty responses. This may indicate content filtering or a model issue.",
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content_type="text",
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)
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except Exception as retry_error:
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logger.warning(f"Retry failed for {self.get_name()}: {retry_error}")
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tool_output = ToolOutput(
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status="error",
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content=f"Model returned empty response and retry failed: {str(retry_error)}",
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content_type="text",
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)
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else:
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# Non-STOP finish reasons are likely actual errors
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logger.warning(
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f"Response blocked or incomplete for {self.get_name()}. Finish reason: {finish_reason}"
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)
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tool_output = ToolOutput(
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status="error",
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content=f"Response blocked or incomplete. Finish reason: {finish_reason}",
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content_type="text",
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)
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# Return the tool output as TextContent
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return [TextContent(type="text", text=tool_output.model_dump_json())]
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