MCP Code Expert System

# MCP Code Expert System - Implementation Status ## Current State Overview The MCP Code Expert System is now structurally sound with a clear separation of concerns and proper encapsulation following the MCP protocol implementation patterns. We've successfully addressed the parameter structure issues. ## What's Working 1. **Tool Registration**: The system properly registers tools with the MCP protocol - Expert tools (`ask_martin` and `ask_bob`) are properly registered - Knowledge graph tools (`read_graph`, `search_nodes`, `open_nodes`) are properly registered 2. **Expert Encapsulation**: Each expert is properly encapsulated in its own module - Experts define their own `input_schema` following JSON Schema format - Experts implement the standardized `ExpertInterface` protocol - Common models are shared through the `experts` package 3. **MCP Protocol Compatibility**: The server implementation follows MCP protocol standards - Using `inputSchema` property instead of `parameters` for tools - Correct initialization options for the MCP server - Proper handling of SSE transport 4. **Communication Flow**: The server receives requests and routes them to the appropriate experts - Tool calls are correctly routed to the corresponding expert implementation - Arguments are properly parsed into `CodeReviewRequest` objects ## Issues Encountered 1. **Pydantic Validation Errors**: When processing Ollama responses ``` Error handling tool call ask_bob: 4 validation errors for CodeReviewResponse review Input should be a valid string [type=string_type, input_value={'cleanCodePrinciples': {...eparation of concerns.'}, input_type=dict] For further information visit https://errors.pydantic.dev/2.10/v/string_type suggestions.0 Input should be a valid string [type=string_type, input_value={'method': 'calculateTota...ction being performed.'}, input_type=dict] For further information visit https://errors.pydantic.dev/2.10/v/string_type suggestions.1 Input should be a valid string [type=string_type, input_value={'method': 'checkout', 'i...ke code more readable.'}, input_type=dict] For further information visit https://errors.pydantic.dev/2.10/v/string_type suggestions.2 Input should be a valid string [type=string_type, input_value={'method': 'addItem', 'im...ing added to the cart.'}, input_type=dict] For further information visit https://errors.pydantic.dev/2.10/v/string_type ``` 2. **Format Mismatch**: Ollama seems to be returning JSON objects where we expect strings - The `review` field is a dictionary object instead of a string - The `suggestions` list contains dictionary objects instead of strings - This causes Pydantic validation to fail when creating the `CodeReviewResponse` 3. **Response Handling**: We need to adapt our Ollama response processing - The `ollama_service.py` needs to be updated to handle this JSON format - The current response parsing doesn't match what Ollama is actually returning ## Data Flow Analysis 1. **Request Flow**: - Client calls `ask_bob` with JavaScript code - Server routes to the correct expert via `experts_by_tool` - Request is validated and converted to `CodeReviewRequest` - Request is passed to the expert's `review_code` method 2. **Processing Flow**: - `RobertCMartinExpert.review_code` calls `ollama_service.get_robert_c_martin_review` - Ollama service returns a result with the review, suggestions, and rating - Expert tries to create a `CodeReviewResponse` with this data - Validation fails because the format doesn't match expectations 3. **Response Format Expected**: ```python CodeReviewResponse( review="string review text", suggestions=["suggestion 1", "suggestion 2"], rating=3 ) ``` 4. **Response Format Received**: ```python { "review": {"cleanCodePrinciples": {...}}, "suggestions": [ {"method": "calculateTotal", ...}, {"method": "checkout", ...}, {"method": "addItem", ...} ], "rating": 3 } ``` ## Next Steps 1. **Update Response Parsing**: Modify the Ollama service to correctly parse the JSON responses 2. **Add Error Handling**: Improve error handling in the expert implementations 3. **Format Conversion**: Add logic to convert dictionary objects to strings if needed 4. **Testing**: Create more thorough tests for different edge cases This snapshot represents the current state of the implementation as of the latest debugging session.