Skip to main content
Glama

MCP Agent Tracker

by Big0290
test_goals_inclusion.pyโ€ข4.16 kB
#!/usr/bin/env python3 """ ๐Ÿงช Test Script: Verify Goals/Project Plans Inclusion """ def test_goals_inclusion(): """Test if project plans/goals are now included""" print("๐Ÿงช TESTING GOALS INCLUSION IN OPTIMIZED PROMPTS") print("=" * 60) try: # Test 1: Import optimized prompt generator print("1๏ธโƒฃ Testing optimized prompt generator import...") from optimized_prompt_generator import OptimizedPromptGenerator generator = OptimizedPromptGenerator() print("โœ… OptimizedPromptGenerator imported successfully") # Test 2: Create test context with project plans print("\n2๏ธโƒฃ Testing context with project plans...") from prompt_generator import PromptContext # Create test context with project plans test_context = PromptContext( conversation_summary="Test conversation summary", action_history="Test action history", tech_stack="Test tech stack", project_plans="๐ŸŽฏ PROJECT PLANS & OBJECTIVES:\n1. Build powerful conversation tracking system โœ…\n2. Implement context-aware prompt processing โœ…\n3. Create intelligent memory management system โœ…\n4. Develop user preference learning โœ…\n5. Build agent metadata system โœ…\n6. Integrate with external AI assistants โœ…\n7. Create seamless prompt enhancement pipeline โœ…\n8. Implement real-time context injection โœ…\n9. Build dynamic instruction processing system โœ…\n10. Create adaptive, learning AI assistant โœ…", user_preferences="Test user preferences", agent_metadata="Test agent metadata", recent_interactions=[], project_patterns=[], best_practices=[], common_issues=[], development_workflow=[], confidence_score=0.9, context_type="test" ) print("โœ… Test context created with project plans") # Test 3: Test context conversion print("\n3๏ธโƒฃ Testing context conversion...") context_dict = generator._context_to_dict(test_context) print(f"โœ… Context converted to dict") print(f"๐Ÿ“‹ Available keys: {list(context_dict.keys())}") print(f"๐ŸŽฏ Project plans available: {'project_plans' in context_dict}") # Test 4: Test intent classification print("\n4๏ธโƒฃ Testing intent classification...") if generator.intent_selector: relevant_context, intent_analysis = generator.intent_selector.select_relevant_context( "test to see if we now have the goals section with project plans", context_dict ) print(f"โœ… Intent classified successfully") print(f"๐ŸŽฏ Intent: {intent_analysis.primary_intent.value}") print(f"๐Ÿ“‹ Context requirements: {intent_analysis.context_requirements}") print(f"๐Ÿ”ง Selected context: {list(relevant_context.keys())}") print(f"๐ŸŽฏ Project plans in selected: {'project_plans' in relevant_context}") else: print("โš ๏ธ Intent selector not available") # Test 5: Test conversation context formatting print("\n5๏ธโƒฃ Testing conversation context formatting...") conversation_context = generator._format_phase1_conversation_context(context_dict) print(f"โœ… Conversation context formatted") print(f"๐Ÿ“‹ Result length: {len(conversation_context)}") print(f"๐ŸŽฏ Contains goals: {'๐ŸŽฏ GOALS:' in conversation_context}") print(f"๐Ÿ“‹ Formatted result:\n{conversation_context}") print("\n" + "=" * 60) print("๐Ÿงช TEST COMPLETE") # Final assessment if '๐ŸŽฏ GOALS:' in conversation_context: print("๐ŸŽ‰ SUCCESS: Goals section is now included!") else: print("โŒ FAILURE: Goals section is still missing!") except Exception as e: print(f"โŒ Error during testing: {e}") import traceback traceback.print_exc() if __name__ == "__main__": test_goals_inclusion()

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/Big0290/MCP'

If you have feedback or need assistance with the MCP directory API, please join our Discord server