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Semantically search and analyze multilingual code with AST-aware insights.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "mcp-vector-search" yet — see the docs or source repo.
Semantically search this codebase for implementations related to “user permission checks and role-based authorization,” prioritizing core functions, call chains, and relevant file paths.
Returns semantically relevant code snippets, file locations, and an explanation of relationships between key functions.
Analyze the execution path in this project from the API entry point to database writes, using AST structure to explain main modules, function call relationships, and potential coupling points.
Outputs a structured call-chain analysis highlighting entry points, key nodes, data flow, and possible refactoring suggestions.
First inspect the repository code related to “cache invalidation strategy,” then summarize the involved modules, configuration items, error handling approach, and test coverage.
Provides an AI-ready codebase summary that supports faster follow-up development and debugging questions.
Analyze codebases with semantic search, dependency insights, and natural language Q&A.
Index codebases semantically and find relevant code using natural language queries.
Search code semantically and answer questions about a codebase.
Search local code with text, symbol, and semantic hybrid retrieval.
Search repositories semantically and turn codebases into AI-ready context and knowledge.
Search and navigate multiple code repositories with natural language understanding.