An MCP server that transforms codebases into knowledge graphs using Neo4J, enabling AI assistants to understand code structure, relationships, and metrics for more context-aware assistance.
A local code-intelligence engine for AI agents that indexes repositories into a PostgreSQL-backed code graph and serves structured, token-budgeted context over MCP and HTTP, enabling targeted queries on symbols, dependencies, contracts, and impact analysis.
Zero-dependency Go MCP server that indexes codebases into an embedded Kùzu graph DB—enabling 90%+ token reduction, precise call-chain queries, and AI architectural health audits.
A minimalist indexing tool that provides AI agents with semantic search and structural AST parsing for deep codebase understanding. It enables autonomous agents to navigate large codebases predictably using vector embeddings and native language server capabilities like definition and reference tracking.
A graph-powered code intelligence engine that indexes codebases into a structural knowledge graph to provide AI agents with deep context on function calls, types, and execution flows. It offers local, zero-dependency tools for hybrid search, impact analysis, and dead code detection across Python, JavaScript, and TypeScript projects.
Provides efficient code navigation and graph-based analysis for AI agents, enabling symbol resolution, callers, implementations, and type schemas with minimal token usage.
Provides a dependency graph of any local repository with tools for change impact, transitive dependents, health audits, and more, enabling AI coding agents to see structure and refactor safely.