Chroma MCP Server

# Usage Guides This section contains detailed guides on how to use specific features and workflows within the `chroma-mcp-server` and its associated client tools. These documents aim to provide practical instructions and conceptual explanations to help you leverage the system effectively. ## Available Guides - **[Daily Workflow Integration](./daily_workflow_integration.md)** - Explains how to integrate the `chroma-mcp-server` ecosystem into your daily development workflow. - **[Automated Test Workflow](./automated_test_workflow.md)** - Explains the fully automated test-driven learning workflow, including setup, how it captures test failures and successes, and its integration with Git hooks and ChromaDB for validated learning promotion. - **[Context Module](./context_module.md)** - Details the `context.py` module, which provides reusable logic for extracting and processing contextual information such as code snippets, diffs, and tool usage sequences. - **[Enhanced Context Capture](./enhanced_context_capture.md)** - Describes the system for automatically extracting rich contextual information (code diffs, tool sequences, confidence scores) during AI interactions that modify code. Also covers the error-driven learning approach. - **[ROI Measurement Framework](./roi_measurement.md)** - Outlines the framework and metrics for measuring the return on investment and effectiveness of the RAG (Retrieval Augmented Generation) implementation. - **[Semantic Code Chunking](./semantic_chunking.md)** - Explains the strategy of preserving logical code structures (functions, classes) when indexing code, leading to more meaningful context retrieval. - **[Test Result Integration](./test_result_integration.md)** - Details how test execution results are captured, stored, and integrated into the RAG workflow to measure code quality improvements and correlate them with development activities. - **[Tool Usage Format Specification](./tool_usage_format.md)** - Specifies the JSON format expected for logging tool usage information, whether captured automatically or provided manually. - **[Validation System](./validation_system.md)** - Describes the evidence-based validation system for promoting learnings, including different types of evidence and the scoring mechanism. - **[Implicit Learning and Analysis](./implicit_learning.md)** - Covers how the system automatically captures data from development activities for analysis and pattern identification, primarily through the `chat_history_v1` collection. - **[Derived Learnings](./derived_learnings.md)** - Explains the `derived_learnings_v1` collection, the process of curating high-quality insights, and how these learnings are used to augment RAG. - **[Troubleshooting Context Capture](./troubleshooting_context_capture.md)** - Provides guidance on diagnosing and resolving common issues with the enhanced context capture system, ensuring rich data logging.