- Resolved TypeError due to passing 4 arguments to a 3-parameter constructor
- Updated instantiation logic in `mock_multi_agent.py` to restore runtime functionality
- Implemented `mock_stock_server.py` to handle stock-related computations
- Added a stock analysis agent in `mock_multi_agent.py` to process financial queries
- Updated `.replit` configuration to support new server integration
- Updated `system_prompt` in `mock_multi_agent.py` to guide user expectations
- Clearly stated supported operations and how agent should respond to capability questions
- Updated system prompt in `mock_multi_agent.py` to reflect only supported tools
- Added error handling in `mock_math_server.py` to reject unsupported operations
- Ensured agent behavior stays within defined context and MCP protocol limits
- Modified `mock_multi_agent.py` to avoid duplicate responses from multiple calculators
- Integrated logic using `agent.py` and `mcp_integration.py` for proper agent-tool mapping
- Updated output formatting and response control for clearer user experience
- Set `gpt-4o-mini` explicitly as model to ensure consistent responses
- Fixed agent configuration and response parsing in `mock_multi_agent.py`
- Updated `mock_math_server.py` and `.replit` config for accurate debugging and logging
- Created `mock_math_server.py` to simulate MCP server behavior
- Developed `mock_multi_agent.py` to orchestrate agent–server interaction
- Added `mock_integration_test.py` to validate end-to-end data flow
- Ensured user input → server request → agent processing → user output pipeline
- Fixed `math_server.py` to support clean server initialization
- Updated `multi_server_test.py` to validate single agent interactions with multiple MCP servers
- Fixed port configuration in `math_server.py` to ensure proper server startup
- Updated response handling in `multi_server_test.py` to resolve missing outputs during testing
- Adjusted `math_server.py` and `calc_server.py` to support many-to-many agent-server interactions
- Resolved config inconsistencies causing agents to fail output delivery
- Ensured each agent can access appropriate tools from multiple MCP servers
- Resolved broken or missing import statements in `math_server.py` and `calc_server.py`
- Updated server initialization to ensure proper startup and output delivery
- Corrected initialization issues in `math_server.py` and `calc_server.py`
- Improved response formatting and delivery in `multi_server_test.py`
- Resolved issue where agent output showed raw stream wrapper instead of actual response
- Improved clarity and readability of agent responses in `multi_server_test.py`
- Added a dedicated function to format multi-agent outputs consistently
- Updated all agents in `multi_server_test.py` to explicitly define model name
- Eliminated LiteLLM fallback warnings during runtime
- Ensured proper agent responses in multi-agent MCP test environment
- Resolved model selection warnings by specifying 'gpt-4o-mini'
- Ensured agents explicitly define model name to avoid default fallbacks
- Improves clarity and consistency in agent–MCP server communication
- Created `multi_server_test.py` to test coordination across agents and servers
- Added `calc_server.py` for handling computation requests
- Referenced swarms-rs Rust architecture for Python-based design structure
- Created proper MCP-compatible `math_server.py`
- Set up `test_integration.py` with multi-agent system structure
- Updated `.replit` config for seamless client-server testing
- Updated `math_server.py` and `test_integration.py` to explicitly use 'gpt-4o-mini'
- Ensures consistent model configuration across test and runtime environments
- Enhanced `math_server.py` to handle invalid tool requests and unknown inputs gracefully
- Updated `test_integration.py` to include edge case scenarios for validation
- Ensured agents dynamically discover available tools and respond accordingly
Pavan Kumar
DP37
Kye Gomez
Kye Gomez
ascender1729