Files
my-pal-mcp-server/simulator_tests/test_consensus_conversation.py
Beehive Innovations 95556ba9ea 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>
2025-06-17 10:53:17 +04:00

223 lines
9.3 KiB
Python

#!/usr/bin/env python3
"""
Consensus Conversation Continuation Test
Tests that the consensus tool properly handles conversation continuation
and builds conversation context correctly when using continuation_id.
"""
import json
import subprocess
from .base_test import BaseSimulatorTest
class TestConsensusConversation(BaseSimulatorTest):
"""Test consensus tool conversation continuation functionality"""
@property
def test_name(self) -> str:
return "consensus_conversation"
@property
def test_description(self) -> str:
return "Test consensus tool conversation building and continuation"
def get_docker_logs(self):
"""Get Docker container logs"""
try:
result = subprocess.run(
["docker", "logs", "--tail", "100", self.container_name], capture_output=True, text=True, timeout=30
)
if result.returncode == 0:
return result.stdout.split("\n")
else:
self.logger.warning(f"Failed to get Docker logs: {result.stderr}")
return []
except Exception as e:
self.logger.warning(f"Exception getting Docker logs: {e}")
return []
def run_test(self) -> bool:
"""Test consensus conversation continuation"""
try:
self.logger.info("Testing consensus tool conversation continuation")
# Setup test files for context
self.setup_test_files()
# Phase 1: Start conversation with chat tool (which properly creates continuation_id)
self.logger.info("Phase 1: Starting conversation with chat tool")
initial_response, continuation_id = self.call_mcp_tool(
"chat",
{
"prompt": "Please use low thinking mode. I'm working on a web application and need advice on authentication. Can you look at this code?",
"files": [self.test_files["python"]],
"model": "local-llama",
},
)
# Validate initial response
if not initial_response:
self.logger.error("Failed to get initial chat response")
return False
if not continuation_id:
self.logger.error("Failed to get continuation_id from initial chat")
return False
self.logger.info(f"Initial chat response preview: {initial_response[:200]}...")
self.logger.info(f"Got continuation_id: {continuation_id}")
# Phase 2: Use consensus with continuation_id to test conversation building
self.logger.info("Phase 2: Using consensus with continuation_id to test conversation building")
consensus_response, _ = self.call_mcp_tool(
"consensus",
{
"prompt": "Based on our previous discussion about authentication, I need expert consensus: Should we implement OAuth2 or stick with simple session-based auth?",
"models": [
{
"model": "local-llama",
"stance": "for",
"stance_prompt": "Focus on OAuth2 benefits: security, scalability, and industry standards.",
},
{
"model": "local-llama",
"stance": "against",
"stance_prompt": "Focus on OAuth2 complexity: implementation challenges and simpler alternatives.",
},
],
"continuation_id": continuation_id,
"model": "local-llama",
},
)
# Validate consensus response
if not consensus_response:
self.logger.error("Failed to get consensus response with continuation_id")
return False
self.logger.info(f"Consensus response preview: {consensus_response[:300]}...")
# Log the full response for debugging if it's not JSON
if not consensus_response.startswith("{"):
self.logger.error(f"Consensus response is not JSON. Full response: {consensus_response}")
return False
# Parse consensus response
try:
consensus_data = json.loads(consensus_response)
except json.JSONDecodeError:
self.logger.error(f"Failed to parse consensus response as JSON. Full response: {consensus_response}")
return False
if consensus_data.get("status") != "consensus_success":
self.logger.error(f"Consensus failed with status: {consensus_data.get('status')}")
if "error" in consensus_data:
self.logger.error(f"Error: {consensus_data['error']}")
return False
# Phase 3: Check server logs for conversation building
self.logger.info("Phase 3: Checking server logs for conversation building")
# Check for conversation-related log entries
logs = self.get_docker_logs()
if not logs:
self.logger.warning("Could not retrieve Docker logs for verification")
else:
# Look for conversation building indicators
conversation_logs = [
line
for line in logs
if any(
keyword in line
for keyword in [
"CONVERSATION HISTORY",
"continuation_id",
"build_conversation_history",
"ThreadContext",
f"thread:{continuation_id}",
]
)
]
if conversation_logs:
self.logger.info(f"Found {len(conversation_logs)} conversation-related log entries")
# Show a few examples (truncated)
for i, log in enumerate(conversation_logs[:3]):
self.logger.info(f" Conversation log {i+1}: {log[:100]}...")
else:
self.logger.warning(
"No conversation-related logs found (may indicate conversation not properly built)"
)
# Check for any ERROR entries related to consensus
error_logs = [
line
for line in logs
if "ERROR" in line
and any(keyword in line for keyword in ["consensus", "conversation", continuation_id])
]
if error_logs:
self.logger.error(f"Found {len(error_logs)} error logs related to consensus conversation:")
for error in error_logs:
self.logger.error(f" ERROR: {error}")
return False
# Phase 4: Verify response structure
self.logger.info("Phase 4: Verifying consensus response structure")
# Check that consensus has proper models_used
models_used = consensus_data.get("models_used", [])
if not models_used:
self.logger.error("Consensus response missing models_used")
return False
# Check that we have responses
responses = consensus_data.get("responses", [])
if not responses:
self.logger.error("Consensus response missing responses")
return False
# Verify at least one successful response
successful_responses = [r for r in responses if r.get("status") == "success"]
if not successful_responses:
self.logger.error("No successful responses in consensus")
return False
self.logger.info(f"Consensus used models: {models_used}")
self.logger.info(f"Consensus had {len(successful_responses)} successful responses")
# Phase 5: Cross-tool continuation test
self.logger.info("Phase 5: Testing cross-tool continuation from consensus")
# Try to continue the conversation with a different tool
chat_response, _ = self.call_mcp_tool(
"chat",
{
"prompt": "Based on our consensus discussion about authentication, can you summarize the key points?",
"continuation_id": continuation_id,
"model": "local-llama",
},
)
if not chat_response:
self.logger.warning("Cross-tool continuation from consensus failed")
# Don't fail the test for this - it's a bonus check
else:
self.logger.info("✓ Cross-tool continuation from consensus working")
self.logger.info(f"Chat continuation preview: {chat_response[:200]}...")
self.logger.info("✓ Consensus conversation continuation test completed successfully")
return True
except Exception as e:
self.logger.error(f"Consensus conversation test failed with exception: {str(e)}")
import traceback
self.logger.error(f"Traceback: {traceback.format_exc()}")
return False
finally:
self.cleanup_test_files()