An open-source Model Context Protocol (MCP) server called "Context+" has been released that converts large codebases into searchable, hierarchical feature graphs using semantic analysis. It is distributed under the MIT license on ForLoopCodes/contextplus on GitHub and is implemented in TypeScript.
MCP Server · Codebase Intelligence
Context+ turns whole codebases into searchable "feature graphs"
An open-source Model Context Protocol server that fuses RAG, Tree-sitter AST parsing, spectral clustering and Obsidian-style wikilinks — structuring code by feature , not by folder, so coding agents get full context in one tool call.
99%
Accuracy claimed by the repository
17
MCP tools across discovery, analysis, code ops & memory
43
File extensions parsed via Tree-sitter
14+
Languages: TypeScript, Python, Rust, Go & more
The 17 tools, organized by purpose
Discovery
AST structure trees, signature-only skeletons, semantic search & spectral-cluster navigation
Analysis
Blast-radius symbol tracking & native tsc / eslint / cargo check
Code Ops
Rule-validated commits with shadow restore points & wikilink hubs
Memory / RAG
Memory nodes, graph traversal & decay-scored pruning of stale links
How code reaches an agent
A local, offline-capable pipeline — launched via bunx contextplus
→
Cluster
Spectral by feature
→
Link
Wikilink + memory graph
→
Seen as a differentiator
Surfaces files that are semantically related but not linked by imports
Spectral clustering + wikilinks + memory graph in one server
Runs locally & offline; shadow undo without touching git history
Open questions
Public benchmarks are scarce; the "99%" claim isn't spelled out
Reproducibility unclear — details left to test code
Developers want measured data on large monorepos
The bigger shift: alongside tools like GraphMind and codemaps, "context graphs / feature graphs" are emerging as a new layer of the AI coding stack heading into 2026 — and Context+ stakes its claim as one MCP community server.
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