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
The article promotes MCP server codebase intelligence from Pharaoh, giving AI agents architectural truth via a live code graph rather than blind scraping. It lists nine upgrades: knowledge-graph mapping, function search, blast-radius, reachability, dead-code detection, vision-gap analysis, consolida
Codebase Knowledge Graph MCPs convert a codebase into a precomputed, deterministic graph of facts (functions, imports, env vars) that AI agents query for architectural context. This reduces hallucinations, lowers token costs, and enables privacy-preserving local or tenant-isolated hosting for small
Context window limits degrade AI coding quality, causing missed files and duplicated logic as you reach practical tokens (4K–8K) and multiple sources. Bigger windows don’t guarantee better results; context rot and recency bias erode recall before the cap. The piece argues for architectural intel
The article argues that AI coding tools fail when they only treat code as isolated text; true reliability comes from giving agents full codebase context via knowledge graphs and a Model Context Protocol (MCP). It promotes building a code knowledge graph (Neo4j) from TS/Python to map functions, modul
AI coding tools struggle when they only read files, missing architecture, dependencies, and cross-service links in large repos. It promotes deterministic parsing and knowledge graphs to give LLMs true context and unlock graph-based queries. Pharaoh and MCP enable live, queryable repo graphs that int
AI coding assistants that see files in isolation breed architectural drift, duplicates, hidden blast radii, and unsafe suggestions. A structure-first workflow builds a live codebase map with module profiles, blast-radius and reachability graphs, and global function searches; enforces spec-to-code al
Claude code context windows often hit a limit, causing forgotten files, duplicated code, and dropped context during real work. Expanding the window isn’t a fix: code is token-heavy, histories accumulate, and latency and cost rise. The cure is disciplined context: modular sessions, /clear and /compac
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