- 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
Kye Gomez