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Analyze codebases with semantic graphs for impact, context, and PR reviews.
Copy the install command and let the AI configure it · recommended for beginners
Please install the "io.github.codegraph-ai/codegraph" MCP server from askskill: Run: claude mcp add 'io-github-codegraph-ai-codegraph' -- npx -y @astudioplus/codegraph-mcp
Using this repository's semantic code graph, analyze the upstream callers and downstream callees of the `processOrder` function, and list key dependencies and file locations by layer.
A structured call graph showing related functions, dependency layers, and file locations for quick code understanding.
If I modify the token validation logic in `UserAuthService`, use the code graph to identify potentially affected modules, APIs, tests, and risk points, then provide validation suggestions.
A list of impacted areas, risk notes, and recommended validation steps to implement the change safely.
Review this PR and, using the semantic code graph, summarize the core changes, potentially broken call relationships, missing tests, and code sections that need extra attention.
A review-focused summary with risk points, test gaps, and priority recommendations to improve PR review efficiency.
Provide structured code context and dependency graphs for efficient codebase understanding.
Query, understand, and edit large multilingual codebases with AI knowledge graphs.
Run local semantic search and call graph analysis across codebases.
Query and understand large codebases with a fast knowledge graph for AI agents.
Improve code review efficiency with a searchable knowledge graph and configurable embeddings.
Help AI agents search, understand, and operate on codebases with a content-addressed graph.