Add DocGen tool with comprehensive documentation generation capabilities (#109)

* WIP: new workflow architecture

* WIP: further improvements and cleanup

* WIP: cleanup and docks, replace old tool with new

* WIP: cleanup and docks, replace old tool with new

* WIP: new planner implementation using workflow

* WIP: precommit tool working as a workflow instead of a basic tool
Support for passing False to use_assistant_model to skip external models completely and use Claude only

* WIP: precommit workflow version swapped with old

* WIP: codereview

* WIP: replaced codereview

* WIP: replaced codereview

* WIP: replaced refactor

* WIP: workflow for thinkdeep

* WIP: ensure files get embedded correctly

* WIP: thinkdeep replaced with workflow version

* WIP: improved messaging when an external model's response is received

* WIP: analyze tool swapped

* WIP: updated tests
* Extract only the content when building history
* Use "relevant_files" for workflow tools only

* WIP: updated tests
* Extract only the content when building history
* Use "relevant_files" for workflow tools only

* WIP: fixed get_completion_next_steps_message missing param

* Fixed tests
Request for files consistently

* Fixed tests
Request for files consistently

* Fixed tests

* New testgen workflow tool
Updated docs

* Swap testgen workflow

* Fix CI test failures by excluding API-dependent tests

- Update GitHub Actions workflow to exclude simulation tests that require API keys
- Fix collaboration tests to properly mock workflow tool expert analysis calls
- Update test assertions to handle new workflow tool response format
- Ensure unit tests run without external API dependencies in CI

🤖 Generated with [Claude Code](https://claude.ai/code)

Co-Authored-By: Claude <noreply@anthropic.com>

* WIP - Update tests to match new tools

* WIP - Update tests to match new tools

* WIP - Update tests to match new tools

* Should help with https://github.com/BeehiveInnovations/zen-mcp-server/issues/97
Clear python cache when running script: https://github.com/BeehiveInnovations/zen-mcp-server/issues/96
Improved retry error logging
Cleanup

* WIP - chat tool using new architecture and improved code sharing

* Removed todo

* Removed todo

* Cleanup old name

* Tweak wordings

* Tweak wordings
Migrate old tests

* Support for Flash 2.0 and Flash Lite 2.0

* Support for Flash 2.0 and Flash Lite 2.0

* Support for Flash 2.0 and Flash Lite 2.0
Fixed test

* Improved consensus to use the workflow base class

* Improved consensus to use the workflow base class

* Allow images

* Allow images

* Replaced old consensus tool

* Cleanup tests

* Tests for prompt size

* New tool: docgen
Tests for prompt size
Fixes: https://github.com/BeehiveInnovations/zen-mcp-server/issues/107
Use available token size limits: https://github.com/BeehiveInnovations/zen-mcp-server/issues/105

* Improved docgen prompt
Exclude TestGen from pytest inclusion

* Updated errors

* Lint

* DocGen instructed not to fix bugs, surface them and stick to d

* WIP

* Stop claude from being lazy and only documenting a small handful

* More style rules

---------

Co-authored-by: Claude <noreply@anthropic.com>

providers/base.py

@@ -1,10 +1,13 @@
 """Base model provider interface and data classes."""
 
+import logging
 from abc import ABC, abstractmethod
 from dataclasses import dataclass, field
 from enum import Enum
 from typing import Any, Optional
 
+logger = logging.getLogger(__name__)
+
 
 class ProviderType(Enum):
     """Supported model provider types."""
@@ -228,6 +231,46 @@ class ModelProvider(ABC):
         """Validate if the model name is supported by this provider."""
         pass
 
+    def get_effective_temperature(self, model_name: str, requested_temperature: float) -> Optional[float]:
+        """Get the effective temperature to use for a model given a requested temperature.
+
+        This method handles:
+        - Models that don't support temperature (returns None)
+        - Fixed temperature models (returns the fixed value)
+        - Clamping to min/max range for models with constraints
+
+        Args:
+            model_name: The model to get temperature for
+            requested_temperature: The temperature requested by the user/tool
+
+        Returns:
+            The effective temperature to use, or None if temperature shouldn't be passed
+        """
+        try:
+            capabilities = self.get_capabilities(model_name)
+
+            # Check if model supports temperature at all
+            if hasattr(capabilities, "supports_temperature") and not capabilities.supports_temperature:
+                return None
+
+            # Get temperature range
+            min_temp, max_temp = capabilities.temperature_range
+
+            # Clamp to valid range
+            if requested_temperature < min_temp:
+                logger.debug(f"Clamping temperature from {requested_temperature} to {min_temp} for model {model_name}")
+                return min_temp
+            elif requested_temperature > max_temp:
+                logger.debug(f"Clamping temperature from {requested_temperature} to {max_temp} for model {model_name}")
+                return max_temp
+            else:
+                return requested_temperature
+
+        except Exception as e:
+            logger.debug(f"Could not determine effective temperature for {model_name}: {e}")
+            # If we can't get capabilities, return the requested temperature
+            return requested_temperature
+
     def validate_parameters(self, model_name: str, temperature: float, **kwargs) -> None:
         """Validate model parameters against capabilities.