Add Consensus Tool for Multi-Model Perspective Gathering (#67)
* WIP Refactor resolving mode_names, should be done once at MCP call boundary Pass around model context instead Consensus tool allows one to get a consensus from multiple models, optionally assigning one a 'for' or 'against' stance to find nuanced responses. * Deduplication of model resolution, model_context should be available before reaching deeper parts of the code Improved abstraction when building conversations Throw programmer errors early * Guardrails Support for `model:option` format at MCP boundary so future tools can use additional options if needed instead of handling this only for consensus Model name now supports an optional ":option" for future use * Simplified async flow * Improved model for request to support natural language Simplified async flow * Improved model for request to support natural language Simplified async flow * Fix consensus tool async/sync patterns to match codebase standards CRITICAL FIXES: - Converted _get_consensus_responses from async to sync (matches other tools) - Converted store_conversation_turn from async to sync (add_turn is synchronous) - Removed unnecessary asyncio imports and sleep calls - Fixed ClosedResourceError in MCP protocol during long consensus operations PATTERN ALIGNMENT: - Consensus tool now follows same sync patterns as all other tools - Only execute() and prepare_prompt() are async (base class requirement) - All internal operations are synchronous like analyze, chat, debug, etc. TESTING: - MCP simulation test now passes: consensus_stance ✅ - Two-model consensus works correctly in ~35 seconds - Unknown stance handling defaults to neutral with warnings - All 9 unit tests pass (100% success rate) The consensus tool async patterns were anomalous in the codebase. This fix aligns it with the established synchronous patterns used by all other tools while maintaining full functionality. 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com> * Fixed call order and added new test * Cleanup dead comments Docs for the new tool Improved tests --------- Co-authored-by: Claude <noreply@anthropic.com>
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simulator_tests/test_consensus_three_models.py
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153
simulator_tests/test_consensus_three_models.py
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"""
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Test consensus tool with three models demonstrating sequential processing
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"""
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import json
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from .base_test import BaseSimulatorTest
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class TestConsensusThreeModels(BaseSimulatorTest):
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"""Test consensus tool functionality with three models (testing sequential processing)"""
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@property
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def test_name(self) -> str:
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return "consensus_three_models"
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@property
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def test_description(self) -> str:
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return "Test consensus tool with three models using flash:against, flash:for, local-llama:neutral"
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def run_test(self) -> bool:
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"""Run three-model consensus test"""
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try:
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self.logger.info("Testing consensus tool with three models: flash:against, flash:for, local-llama:neutral")
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# Send request with three ModelConfig objects
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response, continuation_id = self.call_mcp_tool(
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"consensus",
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{
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"prompt": "Is a sync manager class a good idea for my CoolTodos app?",
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"models": [
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{
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"model": "flash",
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"stance": "against",
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"stance_prompt": "You are a software architecture critic. Focus on the potential downsides of adding a sync manager class: complexity overhead, maintenance burden, potential for over-engineering, and whether simpler alternatives exist. Consider if this adds unnecessary abstraction layers.",
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},
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{
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"model": "flash",
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"stance": "for",
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"stance_prompt": "You are a software architecture advocate. Focus on the benefits of a sync manager class: separation of concerns, testability, maintainability, and how it can improve the overall architecture. Consider scalability and code organization advantages.",
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},
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{
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"model": "local-llama",
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"stance": "neutral",
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"stance_prompt": "You are a pragmatic software engineer. Provide a balanced analysis considering both the benefits and drawbacks. Focus on the specific context of a CoolTodos app and what factors would determine if this is the right choice.",
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},
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],
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"model": "flash", # Default model for Claude's synthesis
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"focus_areas": ["architecture", "maintainability", "complexity", "scalability"],
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},
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)
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# Validate response
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if not response:
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self.logger.error("Failed to get response from three-model consensus tool")
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return False
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self.logger.info(f"Three-model consensus response preview: {response[:500]}...")
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# Parse the JSON response
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try:
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consensus_data = json.loads(response)
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except json.JSONDecodeError:
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self.logger.error(f"Failed to parse three-model consensus response as JSON: {response}")
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return False
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# Validate consensus structure
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if "status" not in consensus_data:
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self.logger.error("Missing 'status' field in three-model consensus response")
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return False
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if consensus_data["status"] != "consensus_success":
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self.logger.error(f"Three-model consensus failed with status: {consensus_data['status']}")
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# Log additional error details for debugging
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if "error" in consensus_data:
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self.logger.error(f"Error message: {consensus_data['error']}")
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if "models_errored" in consensus_data:
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self.logger.error(f"Models that errored: {consensus_data['models_errored']}")
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if "models_skipped" in consensus_data:
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self.logger.error(f"Models skipped: {consensus_data['models_skipped']}")
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if "next_steps" in consensus_data:
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self.logger.error(f"Suggested next steps: {consensus_data['next_steps']}")
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return False
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# Check that models were used correctly
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if "models_used" not in consensus_data:
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self.logger.error("Missing 'models_used' field in three-model consensus response")
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return False
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models_used = consensus_data["models_used"]
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self.logger.info(f"Models used in three-model test: {models_used}")
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# Validate we got the expected models (allowing for some to fail)
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expected_models = ["flash:against", "flash:for", "local-llama"]
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successful_models = [m for m in expected_models if m in models_used]
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if len(successful_models) == 0:
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self.logger.error("No models succeeded in three-model consensus test")
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return False
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self.logger.info(f"Successful models in three-model test: {successful_models}")
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# Validate responses structure
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if "responses" not in consensus_data:
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self.logger.error("Missing 'responses' field in three-model consensus response")
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return False
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responses = consensus_data["responses"]
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if len(responses) == 0:
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self.logger.error("No responses received in three-model consensus test")
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return False
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self.logger.info(f"Received {len(responses)} responses in three-model test")
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# Count successful responses by stance
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stance_counts = {"for": 0, "against": 0, "neutral": 0}
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for resp in responses:
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if resp.get("status") == "success":
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stance = resp.get("stance", "neutral")
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stance_counts[stance] = stance_counts.get(stance, 0) + 1
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self.logger.info(f"Stance distribution: {stance_counts}")
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# Verify we have at least one successful response
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total_successful = sum(stance_counts.values())
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if total_successful == 0:
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self.logger.error("No successful responses in three-model consensus test")
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return False
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# Check for sequential processing indication (>2 models should use sequential)
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if len(consensus_data["models_used"]) > 2:
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self.logger.info("✓ Sequential processing was correctly used for >2 models")
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else:
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self.logger.info("✓ Concurrent processing was used (≤2 models)")
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# Verify synthesis guidance is present
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if "next_steps" not in consensus_data:
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self.logger.error("Missing 'next_steps' field in three-model consensus response")
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return False
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self.logger.info("✓ Three-model consensus tool test completed successfully")
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self.logger.info(f"✓ Total successful responses: {total_successful}")
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self.logger.info(
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f"✓ Stance diversity achieved: {len([s for s in stance_counts.values() if s > 0])} different stances"
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)
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return True
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except Exception as e:
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self.logger.error(f"Three-model consensus test failed with exception: {str(e)}")
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return False
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