CSCSoftware/AiDex

**The persistent brain for AI coding agents.**
AiDex is an MCP server that gives AI coding assistants a memory, semantic search, and live telemetry — local-first, model-agnostic. Works with any MCP-compatible AI assistant: Claude Code, Claude Desktop, Cursor, Windsurf, Gemini CLI, VS Code Copilot, and more.
Three Pillars
🧠 **Memory** — Tasks, notes, and session-notes survive every chat. Auto-logged history, scheduled tasks, cross-session continuity. Your AI knows tomorrow what mattered today.
🔍 **Search** — Three modes: exact (identifier), semantic (concept), hybrid (RRF fusion of both). Embeds code, docs, and workspace items into one ranking. Cross-project — every repo in one query. Optional LLM layer translates non-English queries and reranks results.
🌐 **Telemetry** — LogHub receives live logs from any app via HTTP (no SDK). The AI watches what your code actually does, not just what it says. Live-streamed in the Viewer.
And yes — it's still 50× more token-efficient than grep.
| | Without AiDex | With AiDex |
|---|---|---|
| Find PlayerHealth | Grep → 200 hits in 40 files → reads 5 files → **2,000+ tokens** | 1 query → 3 exact locations → **~50 tokens** |
| Get file structure | Reads entire 500-line file → **1,500 tokens** | Signatures → classes + methods → **~80 tokens** |
| What changed today? | git diff + grep + context → **3,000+ tokens** | Time-filtered query → **~50 tokens** |
What's Inside — 33 Tools in One Server
| Category | Tools | What it does |
|----------|-------|--------------|
| **Semantic Search** 🆕 | search, settings | Hybrid / semantic / exact retrieval over code, docs & workspace. Settings tab to configure embeddings + LLM layer |
| **Index & Identifier Search** | init, query, update, remove, status | Index your project, search identifiers by name (exact/contains/starts_with), time-based filtering |
| **Signatures** | signature, signatures | Get classes + methods of any file without reading it — single file or glob pattern |
| **Project Overview** | summary, tree, describe, files | Entry points, language breakdown, file tree with stats, file listing by type |
| **Cross-Project** | link, unlink, links, scan | Link dependencies, discover indexed projects |
| **Global Search** | global_init, global_query, global_signatures, global_status, global_refresh | Search identifiers across ALL your projects — "Have I ever written X?" |
| **Guidelines** | global_guideline | Persistent AI instructions & coding conventions — shared across all projects |
| **Sessions** | session, note | Track sessions, detect external changes, leave notes for next session (with searchable history) |
| **Task Backlog** | task, tasks | Built-in task management with priorities, tags, auto-logged history, and **scheduled/recurring tasks** |
| **Log Hub** | log | Universal log receiver — any program sends logs via HTTP, queryable by the AI, live in Viewer |
| **Screenshots** | screenshot, windows | Cross-platform screen capture with LLM optimization — scale + color reduction saves up to 95% tokens |
| **Viewer** | viewer | Interactive browser UI with file tree, signatures, tasks, logs, search, and live reload |
**14 languages** — C#, TypeScript, JavaScript, Rust, Python, C, C++, Java, Go, PHP, Ruby, HCL/Terraform, Kotlin, Swift — plus Astro frontmatter
Quick Examples — see it in action
Table of Contents
- What's Inside
- Semantic Search & LLM Layer 🆕
- The Problem
- The Solution
- Why Not Just Grep?
- How It Works
- Features
- Supported Languages
- Quick Start
- Available Tools
- Time-based Filtering
- Project Structure
- Session Notes
- Task Backlog
- Global Search
- AI Guidelines
- Log Hub
- Debug Dashboard
- Screenshots — LLM-Optimized
- Interactive Viewer
- CLI Usage
- Performance
- Technology
- Contributing
- License
Semantic Search & LLM Layer
**v2.0** added semantic search via locally-run embeddings — your AI can find a function even when it doesn't know the exact identifier.
Three modes — pick the right tool for the question
| Mode | What it does | When to use |
|------|--------------|-------------|
| exact | Identifier match (same as aidex_query) | You know the name. PlayerHealth → 3 hits |
| semantic | Vector KNN over embedded code+docs+workspace | You know the *concept*. "how do we cache the model" → finds getQueryEmbedder |
| hybrid (default) | RRF fusion of both | Mixed queries. Robust by default |
What gets embedded
- **Code** — every method and type, three-tier chunking (signature + doc-comment + weighted identifier bag)