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Build and query config-aware code graphs for context-aware code analysis.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "CRAG-MCP" yet — see the docs or source repo.
Build a code graph for this repository and enable conditional compilation filtering for FEATURE_X and PLATFORM_LINUX. Then analyze which key modules are called by the function init_runtime under this configuration and output the call chain.
A filtered call-chain analysis showing the effective functions and module relationships under the specified configuration.
Query the execution path of request_handler under ENABLE_CACHE=1 and ENABLE_CACHE=0, compare which branches, functions, and files change, and summarize the differences.
A comparison of code-path differences between the two build configurations, highlighting behavior changes caused by conditional compilation.
Query all code nodes affected by the macro USE_LEGACY_PARSER, identify dependent functions, files, and upstream callers, and list the areas that should be checked during refactoring.
A structured impact map for the macro, helping assess modification or refactoring risks.
Query code structure and cross-language relationships via MCP with auditable access logs.
Analyze code structure and Git history while drastically reducing AI token usage.
Analyze, match, and transform code structures across multiple programming languages.
Index local repositories for semantic search and structured code understanding.
Read, edit, and refactor code precisely with AST-based, token-efficient operations.
Provide coding standards context so AI-generated code follows team conventions.