Overview
deusdata/codebase-memory-mcp addresses the cost of reconstructing relationships in large repositories. It indexes a selected scope into a persistent, typed graph, then exposes orientation, search, tracing, exact-source retrieval, and coverage checks. The series follows construction, evidence-chain reasoning, type-aware and derived enrichment, freshness and atomic publication, and responsible adoption. Its value is a bounded structural context layer that reduces repeated discovery; it is not source code, an embedded answerer, proof of completeness, or a substitute for judgment when scope, resolution, freshness, or runtime evidence is limited.
Learning outcomes
- codebase-memory-mcp reduces repeated structural discovery by making qualified identities and typed relationships reusable across questions.
- A graph result becomes useful evidence only when scope, identity, relationship type, response boundaries, and source verification align.
- Type-aware resolution, semantic search, similarity, and service matching add different evidence; none should be treated as a local call or runtime proof.
- Freshness checks, coverage records, staged publication, and human judgment keep a compact graph from becoming an overconfident answer.
Keywords
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PlansFAQ
What does deusdata/codebase-memory-mcp add beyond file-by-file search?
It provides a persistent, typed map of repository identities and relationships, so an agent can orient, trace structure, and reach exact source without rebuilding the same map each time. It does not replace source or judgment.
How should an AI agent use a codebase-memory-mcp graph result?
The agent should orient within the indexed project, find and trace the relevant relationship, retrieve exact source for material claims, and check coverage, truncation, and generation state before concluding.
Can a codebase-memory-mcp result prove that no callers or impacts exist?
No. An empty or bounded result supports only a scope-qualified conclusion after identity, relationship type, response state, and coverage are checked; excluded, stale, partial, dynamic, or unresolved evidence can require source review.
What do type, semantic, similarity, and service layers each contribute?
Type-aware resolution can strengthen identity and calls; semantic search bridges vocabulary; similarity finds structural near-clones; service matching connects route or channel evidence across boundaries. They remain distinct from exact behavior and runtime proof.