docs: add one-line install command under Highlights

README.md

@@ -68,12 +68,23 @@ and the honest caveats — is in [`docs/eval.md`](docs/eval.md).
 - **Bring your own model platform.** Built for self-hosted and local models first (any OpenAI-compatible server — Ollama, vLLM, LM Studio, LocalAI), with first-class **OpenAI API** and **Anthropic API** support when you want it.
 - **Symbol-aware chunking.** Indexes *functions, classes, and methods* (Python via `ast`; JS/TS/Go/Rust/Java via tree-sitter), not crude fixed-size blocks — so results point at real code units with `file:line` citations.
 - **Hybrid retrieval, with optional reranking.** Dense vector search **+** BM25 keyword search, fused with Reciprocal Rank Fusion — great at both "what does this *mean*" and exact-identifier lookups. Add an optional local **cross-encoder reranker** (two-stage retrieve-then-rerank, `CODERAG_RERANK=1`, no API key) to sharpen the top results.
-- **Drop-in for AI coding agents.** An **MCP server** (`coderag mcp`) lets Claude Code, Codex, and Cursor search a warm, pre-indexed workspace instead of slow grep/glob/read loops — ranked `path:line` results from a single call, with the index kept live as you edit. Works on a plain file directory too, not just code.
+- **Semantic *and* exact search in one tool belt.** Agents get `search_code` (hybrid, by meaning) **and** `search_files` (ripgrep-backed exact regex/glob, the literal-match complement) — plus loop-detection, pagination, and line-numbered reads with "did you mean?" hints. No more grep/glob/find loops.
+- **Drop-in for AI coding agents — one command.** `coderag install` wires the **MCP server** into **Claude Code**, **Hermes**, and **Codex** (auto-detect or an interactive wizard, idempotent, with backups) so they search a warm, pre-indexed workspace instead of slow grep/glob/read loops — ranked `path:line` results from a single call, index kept live as you edit. Works on a plain file directory too, not just code.
 - **Measured, not guessed.** A built-in **evaluation harness** (`coderag eval`) scores retrieval quality — recall@k, MRR, nDCG@k at file *or* symbol level — and can mine a benchmark straight from your git history. Every default (1:1 hybrid, reranker opt-in, adaptive fusion off) is the choice the harness validated, including across an external repo.
 - **Incremental & live.** Content-hashed indexing only re-embeds files that changed; a debounced watcher keeps the index current as you code. No duplicate or stale vectors.
 - **Built to scale.** Exact `Flat` search for small repos, automatic switch to approximate `IVF` past a threshold so it stays fast at 100k+ chunks.
 - **Five surfaces, one engine.** CLI · Python library · HTTP/REST · web UI · MCP server — all thin wrappers over the same `CodeRAG` object.
 
+### ⚡ One line: install + wire into your agent
+
+Download, install, and register CodeRAG as your coding agent's search tool — in a single command:
+
+```bash
+pip install "coderag[mcp] @ git+https://github.com/Neverdecel/CodeRAG" && coderag install
+```
+
+This installs the engine **and** MCP server, then `coderag install` auto-detects **Claude Code**, **Hermes**, and **Codex** (or launches an interactive wizard) and wires CodeRAG in. Restart your agent and it searches a warm, pre-indexed workspace instead of grepping. Preview without writing any config: `coderag install --print`.
+
 ## 🚀 Quick start
 
 ```bash