MCPs tagged with dependency analysis
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AI Distiller (aid)
Efficient codebase summarization and context extraction for AI code generation.
AI Distiller enables efficient distillation of large codebases by extracting essential context, such as public interfaces and data types, discarding method implementations and non-public details by default. It helps AI agents like Claude, Cursor, and other MCP-compatible tools understand project architecture more accurately, reducing hallucinations and code errors. With configurable CLI options, it generates condensed contexts that fit within AI model limitations, improving code generation accuracy and integration with the Model Context Protocol.
- ⭐ 106
- MCP
- janreges/ai-distiller
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codelogic-mcp-server
Leverage CodeLogic’s dependency data for AI-powered impact analysis.
Codelogic-mcp-server implements an MCP (Model Context Protocol) server, enabling integration of CodeLogic's software dependency data into AI programming assistants. It provides tools for code and database impact assessments by interacting with a CodeLogic server, enhancing context-aware code and database analysis. The server supports integration with popular IDEs, including VS Code and Claude Desktop, using Astral UV/UVX for communication. This solution is designed to bring actionable dependency insights to AI coding workflows.
- ⭐ 31
- MCP
- CodeLogicIncEngineering/codelogic-mcp-server