Context+

Semantic Intelligence for Large-Scale Engineering.

Context+ is an MCP server designed for developers who demand 99% accuracy. By combining Tree-sitter AST parsing, Spectral Clustering, and Obsidian-style linking, Context+ turns a massive codebase into a searchable, hierarchical feature graph.

Video.Project.5.mp4

Tools

Discovery

Tool	Description
get_context_tree	Structural AST tree of a project with file headers and symbol ranges (line numbers for functions/classes/methods). Dynamic pruning shrinks output automatically.
get_file_skeleton	Function signatures, class methods, and type definitions with line ranges, without reading full bodies. Shows the API surface.
semantic_code_search	Search by meaning, not exact text. Uses embeddings over file headers/symbols and returns matched symbol definition lines.
semantic_identifier_search	Identifier-level semantic retrieval for functions/classes/variables with ranked call sites and line numbers.
semantic_navigate	Browse codebase by meaning using spectral clustering. Groups semantically related files into labeled clusters.

Analysis

Tool	Description
get_blast_radius	Trace every file and line where a symbol is imported or used. Prevents orphaned references.
run_static_analysis	Run native linters and compilers to find unused variables, dead code, and type errors. Supports TypeScript, Python, Rust, Go.

Code Ops

Tool	Description
propose_commit	The only way to write code. Validates against strict rules before saving. Creates a shadow restore point before writing.
get_feature_hub	Obsidian-style feature hub navigator. Hubs are .md files with [[wikilinks]] that map features to code files.

Version Control

Tool	Description
list_restore_points	List all shadow restore points created by propose_commit. Each captures file state before AI changes.
undo_change	Restore files to their state before a specific AI change. Uses shadow restore points. Does not affect git.

Setup

Quick Start (npx / bunx)

No installation needed. Add Context+ to your IDE MCP config.

For Claude Code, Cursor, and Windsurf, use mcpServers:

{
  "mcpServers": {
    "contextplus": {
      "command": "bunx",
      "args": ["contextplus"],
      "env": {
        "OLLAMA_EMBED_MODEL": "nomic-embed-text",
        "OLLAMA_CHAT_MODEL": "gemma2:27b",
        "OLLAMA_API_KEY": "YOUR_OLLAMA_API_KEY"
      }
    }
  }
}

For VS Code (.vscode/mcp.json), use servers and inputs:

{
  "servers": {
    "contextplus": {
      "type": "stdio",
      "command": "bunx",
      "args": ["contextplus"],
      "env": {
        "OLLAMA_EMBED_MODEL": "nomic-embed-text",
        "OLLAMA_CHAT_MODEL": "gemma2:27b",
        "OLLAMA_API_KEY": "YOUR_OLLAMA_API_KEY"
      }
    }
  },
  "inputs": []
}

If you prefer npx, use:

• "command": "npx"
• "args": ["-y", "contextplus"]

Or generate the MCP config file directly in your current directory:

npx -y contextplus init claude
bunx contextplus init cursor

Supported coding agent names: claude, cursor, vscode, windsurf.

Config file locations:

IDE	Config File
Claude Code	.mcp.json
Cursor	.cursor/mcp.json
VS Code	.vscode/mcp.json
Windsurf	.windsurf/mcp.json

From Source

npm install
npm run build

node build/index.js                      # analyze current directory
node build/index.js /path/to/my-project  # analyze a specific project

Architecture

Three layers built with TypeScript over stdio using the Model Context Protocol SDK:

Core (src/core/) — Multi-language AST parsing (tree-sitter, 43 extensions), gitignore-aware traversal, Ollama vector embeddings with disk cache, wikilink hub graph.

Tools (src/tools/) — 11 MCP tools exposing structural, semantic, and operational capabilities.

Git (src/git/) — Shadow restore point system for undo without touching git history.

Runtime Cache (.mcp_data/) — created on server startup; stores reusable file, identifier, and call-site embeddings to avoid repeated GPU/CPU embedding work. A realtime tracker refreshes changed files/functions incrementally.

Config

Variable	Default	Description
OLLAMA_EMBED_MODEL	nomic-embed-text	Embedding model
OLLAMA_API_KEY	—	Ollama Cloud API key
OLLAMA_CHAT_MODEL	llama3.2	Chat model for cluster labeling
CONTEXTPLUS_EMBED_BATCH_SIZE	8	Embedding batch size per GPU call, clamped to 5-10
CONTEXTPLUS_EMBED_TRACKER	true	Enable realtime embedding refresh on file changes
CONTEXTPLUS_EMBED_TRACKER_MAX_FILES	8	Max changed files processed per tracker tick, clamped to 5-10
CONTEXTPLUS_EMBED_TRACKER_DEBOUNCE_MS	700	Debounce window before tracker refresh

Test

npm test
npm run test:demo
npm run test:all