TL;DR
Building a context-aware AI assistant requires four components: (1) an IDE sensor (VS Code extension) that captures developer state, (2) a context assembler (Rust binary) that processes files and resolves import graphs, (3) an MCP server that serves the assembled context to AI clients, and (4) a Tauri shell that packages everything as a cross-platform desktop app. This guide covers the architecture of each component and how they communicate.
The Four-Component Architecture
A context-aware AI assistant isn't one program — it's a system of four cooperating components, each optimized for its specific role:
Component 1: IDE Sensor
A lightweight VS Code extension written in TypeScript. Its only job: observe IDE state changes (tab switches, file edits, diagnostics) and forward them to the Rust backend via IPC. No heavy computation. Sub-5ms per event.
Component 2: Context Assembler
A Rust binary that receives IDE state events and computes the full context snapshot: file contents, import graphs, type definitions, dependency versions. Runs in 8-15ms. This is where the performance-critical work happens.
Component 3: MCP Server
The Context Assembler also exposes an MCP server endpoint. AI clients (Cursor, Claude, Windsurf) query this endpoint for context. Standard JSON-RPC protocol over stdio or HTTP. No custom integration needed.
Component 4: Tauri Shell
Tauri wraps the Rust binary as a cross-platform desktop app with a system tray icon, auto-update, and configuration UI. The 'UI' is minimal — status indicator, connection state, and settings panel.
Component Communication Flow
Here's how data flows through the system on every IDE state change:
// Data Flow — Tab Switch Event:
1. Developer switches to new tab in VS Code
2. VS Code fires onDidChangeActiveTextEditor event
3. Extension captures: new file URI, cursor position, visible range
4. Extension sends event to Rust backend via WebSocket IPC
5. Rust backend reads new file + resolves import graph
6. Rust backend updates context snapshot in memory
7. Next MCP query from AI client receives updated context
// Total Latency:
Extension event handling: 3ms
IPC transmission: 1ms
Rust context assembly: 8ms
Total: 12ms — imperceptible to developer
The Rust Context Assembler
The Context Assembler is the heart of the system. Here's its internal architecture:
File Reader
Reads file contents from disk using memory-mapped I/O for zero-copy performance. Handles file encoding detection (UTF-8, UTF-16) automatically. Caches file contents with modification-time invalidation.
Import Graph Resolver
Parses import/require statements using tree-sitter. Resolves aliases (@/ paths, tsconfig paths) by reading tsconfig.json. Walks the import graph up to 3 levels deep to find all transitively referenced files.
Context Optimizer
Prioritizes files for the context window: focused file (highest), directly imported files (high), open-but-not-imported files (medium), transitively imported files (lower). Trims to fit the token budget.
MCP JSON Builder
Packages the optimized context as structured JSON following the MCP resource specification. Each file includes: path, language, content, and role (focused/imported/open). The AI client receives structured, labeled context.
Why Build This? When Context Snipe Already Exists
This architecture guide exists for developers who want to understand how context engines work under the hood — or who want to build custom context pipelines for specialized workflows. If you want a production-ready context engine without building one, Context Snipe implements this exact architecture with polish, security scanning, and ongoing maintenance.
🔧 This architecture. Production-ready. Maintained.
Context Snipe implements the exact four-component architecture described in this guide: VS Code extension sensor, Rust context assembler, MCP server, and Tauri shell. Production-ready, auto-updating, with security scanning included. Start free — no credit card →