AI coding accelerates regressions due to limited context, causing silent breakages and duplication in small teams. The guide promotes an architecture-first approach: model the codebase as a living knowledge graph that agents can query to guide changes. It adds guardrails—blast radius, reachability,
AI agents work best with true codebase context—a structured, graph-backed map of modules, dependencies, and endpoints—not file dumps. Flat context misses links and drifts while inflating token costs; deterministic parsing (Tree-sitter) keeps facts reliable. Pharaoh converts TS/Python repos into a Ne
Dead code detection MCP gives real-time, deterministic identification of unused or unreachable code, making its truth accessible to AI agents and tools like Claude Code and Cursor. It exceeds traditional tools by cross-repo reachability with Tree-sitter and graph databases and eliminates runtime LLM
The piece outlines eight strategies to search, validate, and reuse functions at scale in AI-augmented codebases, emphasizing speed, accuracy, and deterministic results. It centers on Pharaoh search_functions, a Neo4j knowledge graph with MCP that delivers import-path aware results and zero per-query
Code Dependency Graph MCP servers turn a codebase into a deterministic, queryable knowledge graph of modules, functions, and endpoints, giving AI tools a reliable architectural map instead of scattered snippets. They reduce blind spots, enable blast-radius analysis, and improve security by exposing
The article promotes a codebase intelligence platform that gives AI agents architecture-aware, live context via a knowledge graph. This context prevents hidden dependency gaps, reduces duplication, and enables deterministic, safer refactors. Pharaoh offers automatic TS/Python parsing, 13+ graph tool
An MCP server for code understanding turns your codebase into a structured Neo4j knowledge graph that AI agents query for ground-truth architecture (call graphs, ASTs, dependencies). It delivers deterministic answers with zero per-query token cost after indexing and strict tool boundaries via a mani
Pharaoh's codebase map for Claude Code uses a Neo4j-powered graph to provide deterministic, fast insight into a repo's architecture and risk. It offers 16 features, including 2K-token module profiles, re-exports-aware function search, blast radius, production reachability, dead-code evidence, and cr
Blast radius analysis tools map the exact impact of a code change across functions, modules, and downstream services, revealing hidden dependencies before you merge. They use deterministic parsing and knowledge graphs, delivering zero-hallucination, reproducible results for AI agents. Outputs includ
A codebase graph gives AI coding agents true architectural context by mapping functions, modules, and dependencies—not just files. It enables safe refactoring, blast-radius analysis, cross-repo dedup, and production reachability, all anchored in the repo’s structure. Parsed with Tree-sitter into a N
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