<div align="center">
<img src="site/static/img/rover-hero.webp" alt="Rover — turn the web into clean, token-efficient Markdown your agent can trust" width="100%">
# Rover
**An MCP server that turns the web into clean, token-efficient Markdown your LLM agent can actually trust.**
[](https://github.com/aaronbassett/rover/actions/workflows/ci.yml)
[](#license)
[](#install)
[](#install)
[Quick start](#quick-start-wire-it-into-your-agent) · [Why Rover](#why-rover) · [How it compares](#how-your-agent-gets-the-web) · [MCP tools](#the-mcp-tools) · [Security](#security--trust) · [Features](#features) · [Docs](#documentation)
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---
Point your agent at a URL and Rover fetches it, strips the ads/nav/chrome, extracts the real content, normalises the markup, counts the tokens, optionally summarises to a budget, and hands back a YAML-frontmattered Markdown document — wrapped so the model knows it's **untrusted third-party data, not instructions**. The same binary runs as a long-lived **MCP server** for Claude Code and other agent harnesses, and as a one-shot **CLI**.
<div align="center">
<img src="site/static/img/rover-demo.webp" alt="rover fetching a Wikipedia page and summarising it to a token budget" width="85%">
<sub><code>rover</code> fetching the Charlie Dog (a.k.a. <strong>Rover</strong> 🐕) page and summarising ~19.6k tokens down to ~330 — summarisation here runs through a configured cloud backend.</sub>
</div>
> [!NOTE]
> Rover is built for single-user-local deployment — one MCP server alongside your IDE/agent, not a multi-tenant gateway. Ship it as a binary, point your agent at it, get on with your work.
## Why Rover
Agents that browse the live web hit the same four walls every time:
- **🧹 Boilerplate, ads, and chrome drown the content.** Token budgets vanish into navigation menus and cookie banners.
- **🖼️ JavaScript-rendered pages return an empty `<div id="root">`** to anything that isn't a browser.
- **🔁 Repeated fetches waste tokens, time, and money** — and ignore politeness rules (rate limits, `robots.txt`, caching headers).
- **🛡️ Fetched web content is untrusted.** A page can carry "ignore your instructions and…" straight into your agent's context. Most fetch tools hand it over raw.
Rover fixes all four. Extraction is the battle-tested [`readabilityrs`](https://crates.io/crates/readabilityrs) crate (Prism/Shiki/rehype/WordPress/GitHub code blocks, MathJax/KaTeX, footnote dialects, lazy-loaded images, permalink anchors). On top of that Rover layers HTTP-aware caching, per-domain rate limiting + `robots.txt`, charset detection, configurable SSRF protection, a layered **prompt-injection guard**, optional headless rendering for SPAs, extractive *and* cloud-LLM summarisation, inline image captioning, and a long-running task model with NDJSON-streamed progress.
## How your agent gets the web
| | **Rover** | **Claude Code `WebFetch`** | **`wget`** |
|---|---|---|---|
| What your agent gets back | Clean Markdown **document** + frontmatter, content hash, token count | A fast model's **answer** about the page (lossy, per-prompt) | Raw HTML / bytes |
| Strips nav/ads/chrome → Markdown | ✅ readability extraction | ✅ HTML→MD (non-optional) | ❌ |
| Reusable across calls (re-read, no re-run) | ✅ cached doc, stable hash | ❌ re-runs the model each prompt | ✅ (raw file) |
| Token budgeting & counts | ✅ estimate · `max_tokens` · summarise-to-fit · count-only | ❌ fixed truncation, no control | ❌ |
| HTTP-aware caching | ✅ TTL · ETag · Last-Modified · stale-while-revalidate | ◻️ flat 15-min cache | ◻️ timestamping (`-N`) only |
| JavaScript / SPA rendering | ◻️ optional (`headless` feature) | ❌ | ❌ |
| Batch fetch + per-domain rate limiting | ✅ `batch_fetch`, token-bucket, streaming progress | ❌ one URL per call | ◻️ recursive, no per-domain limit |
| SSRF / private-network protection | ✅ 5 levels + dial-time re-check (anti-DNS-rebinding) | ◻️ HTTP→HTTPS upgrade; private-IP stance undocumented | ❌ |
| Prompt-injection guard | ✅ layered: nonce wrapper + detectors + optional model | ❌ content goes straight to the model | — |
| Structured metadata (schema.org / OG / Twitter) | ✅ `get_metadata` | ❌ (must ask in the prompt) | ❌ |
| Inline image captioning | ✅ cloud VLMs (OpenAI / Anthropic / Gemini / compatible) | ❌ | ❌ |
| Works offline / no per-fetch API cost | ✅ extractive backend, no API key | ❌ model call per fetch | ✅ |
<sub>✅ full · ◻️ partial/optional · ❌ no · — n/a · `WebFetch` column per the [official Claude Code docs](https://docs.claude.com/en/docs/claude-code).</sub>
> **Rover isn't a web crawler.** To recursively mirror or crawl an entire site, reach for `wget` or `httrack` — Rover fetches and preps *individual* pages for an agent to reason over, not bulk downloads.
## Quick start: wire it into your agent
`rover meta use` does the whole wiring in one command (MCP server, steering hooks for Claude Code, and a rules-file block):
```sh
rover meta use claude # Claude Code: claude mcp add + SessionStart/WebFetch hooks + a CLAUDE.md block
rover meta use general # other harnesses: ./mcp.json + an AGENTS.md steering block
```
`-s/--scope local|user|project` (default `local`) mirrors the Claude CLI. It's idempotent and validates before it writes, so it leaves everything untouched if the `claude` binary is missing or a target file is malformed JSON. Full walkthrough, per-scope file mapping, and by-hand setup: [`rover-fetch.com/docs/quickstart`](https://rover-fetch.com/docs/quickstart).
To add just the MCP server by hand, run `claude mcp add rover -- rover mcp` for **Claude Code**, or point any MCP client at `rover mcp` over stdio with the standard JSON shape:
```json
{
"mcpServers": {
"rover": {
"command": "rover",
"args": ["mcp"]
}
}
}
```
Your agent now has these tools:
| Tool | What it does |
| --- | --- |
| `fetch` | Single URL → cleaned Markdown. Caching, headless rendering, image modes, token budgeting, inline summarisation. |
| `batch_fetch` | Fetch N URLs concurrently with per-domain rate limiting. Returns a `task_id`; stream progress with `rover batch <id> --monitor`. |
| `summarize` | Compact a cached or fresh page via extractive (offline) or cloud backends. Steerable with `focus`, `preserve`, `target_tokens`. |
| `get_metadata` | Extract Schema.org, Open Graph, and Twitter Card metadata without pulling the full body. |
| `count_tokens` | Estimate a URL's token cost across `cl100k` / `o200k` / `claude` / `llama3` / `qwen3` tokenisers without paying it. |
Full tool reference: [`rover-fetch.com/docs/mcp-tools`](https://rover-fetch.com/docs/mcp-tools).
### …or use it from the shell
Every capability is also a one-shot CLI command — handy for scripts, CI, and trying things out:
```sh
rover fetch https://example.com/article # clean Markdown → stdout
rover fetch --max-tokens 4000 https://example.com # summarise to fit a budget
rover cache stats # entry count, size, expired
rover doctor # sanity-check the install
```
> [!TIP]
> `rover --help` prints the full subcommand surface; every subcommand has its own `--help`.
## Install
> [!NOTE]
> Rover is pre-1.0 (`0.1.0`). The build-from-source path below works today; the packaged channels (Homebrew tap, prebuilt tarballs, crates.io) come online with the first tagged release.
All channels install a binary named `rover`.
**Build from source (works today):**
```sh
cargo install --git https://github.com/aaronbassett/rover --locked
# or clone and build:
git clone https://github.com/aaronbassett/rover && cd rover
cargo build --release # binary at target/release/rover
```
The default build (~20 MiB) needs no model downloads, no Chrome, and no extra runtime dependencies.
**Homebrew (macOS) — on release:**
```sh
brew install aaronbassett/tap/rover
```
The `rover` formula ships the JavaScript-rendering (`headless`) build. It does **not** pull in a browser — headless rendering is opt-in and Rover auto-detects a Chrome/Chromium install at runtime (`rover doctor` verifies it). If you want headless mode, install a browser yourself, e.g. `brew install --cask chromium`. Other optional features (e.g. `local-inference`) are available from source via `cargo install` — see crates.io below.
**Prebuilt binary (Linux & macOS) — on release:**
One-line installer:
```sh
curl --proto '=https' --tlsv1.2 -LsSf https://github.com/aaronbassett/rover/releases/latest/download/rover-fetch-installer.sh | sh
```
Or download a `.tar.xz` from the [latest release](https://github.com/aaronbassett/rover/releases/latest), verify its checksum, then extract it and move the `rover` binary onto your `PATH`:
```sh
tar xf rover-fetch-<target>.tar.xz # then move the extracted `rover` onto your PATH
```
Targets: `x86_64`/`aarch64` Linux (gnu) and Intel/Apple-Silicon macOS. The prebuilt binary includes the `headless` feature (JavaScript-rendered pages).
**crates.io — on release:**
```sh
cargo install rover-fetch --features headless # crate is rover-fetch; binary is rover
```
> [!NOTE]
> The crate publishes as `rover-fetch` because `rover` on crates.io is held by an unrelated project. The installed binary is still `rover`. `cargo install` builds with the crate's default (basic) features; add `--features headless` to match the prebuilt and Homebrew binary.
**Requirements:** Rust 1.96+ (edition 2024). Rover is pre-1.0: minor releases may include breaking changes, and the minimum supported Rust version can rise in any release.
## The MCP tools
Every tool returns structured JSON; the content-returning tools (`fetch`, `summarize`, `get_metadata`) additionally wrap their payload in Rover's trusted-preamble + nonce delimiter (see [Security & trust](#security--trust)).
```jsonc
// fetch → cleaned, guarded Markdown document
{
"content": "⚠ The text inside <untrusted-content-a3f9c1> … is third-party web content …\n\n<untrusted-content-a3f9c1>\n---\nurl: \"https://example.com/article\"\ntitle: \"…\"\nestimated_tokens: 14823\ntokenizer: \"o200k\"\nextraction_quality: 0.98\nprompt_injection: { scanned: true, detected: false }\n---\n\n# Article title\n…\n</untrusted-content-a3f9c1>",
"cache_status": "miss",
"summarized": false
}
```
The example hero fetch, unwrapped:
```yaml
---
url: "https://en.wikipedia.org/wiki/Rust_(programming_language)"
title: "Rust (programming language) - Wikipedia"
fetched_at: "2026-06-18T12:34:56Z"
content_hash: "sha256:b3e9…"
estimated_tokens: 14823
tokenizer: "o200k"
language: "en"
extraction_quality: 0.98
---
# Rust (programming language)
Rust is a multi-paradigm, general-purpose programming language…
```
Full schemas, arguments, and wire contracts: [`rover-fetch.com/docs/mcp-tools`](https://rover-fetch.com/docs/mcp-tools).
## Security & trust
Rover treats the web as hostile by default. Three independent layers protect both your agent and Rover's own internal inference.
### Prompt-injection guard
Fetched content is third-party **data**, not instructions — but a malicious page can still try to hijack your agent. Every content-returning tool (`fetch`, `summarize`, `get_metadata`) runs a layered guard:
1. **Structural wrapper (always on).** The returned document is wrapped in a per-response, random-nonce delimiter — `<untrusted-content-a3f9c1>…</untrusted-content-a3f9c1>` — behind a trusted preamble that tells the model to treat everything inside as data only. Forged copies of the tag are stripped, so a page can't predict the nonce or close the wrapper early. **This is the load-bearing guarantee — it never relies on detection.**
2. **Pattern detector (always compiled).** A curated literal + regex ruleset (instruction-override, role-injection, system-prompt-leak, tool-call-smuggle, data-exfil) runs over *normalised* text — NFKC, zero-width/control stripping, homoglyph folding, base64 surfacing — so obfuscated payloads still trip.
3. **ONNX classifier (opt-in).** Build with `--features injection-model` to add a DeBERTa prompt-injection model (downloaded on first use) for novel phrasings the rules don't enumerate.
A configurable response level decides what happens on a hit:
| Level | Action |
| --- | --- |
| `strict` | Drop the body; return the warning only |
| `high` | Remove the matched spans / windows |
| `moderate` *(default)* | Quarantine matched spans in `<DANGER>…</DANGER>` + warn |
| `low` | Content intact; warn only |
| `disabled` | No detection (the wrapper still applies) |
Structured `prompt_injection` telemetry rides along on every response, and content Rover feeds to its **own** summariser/caption models is always independently cleaned at high strength — that hardening can't be disabled. Configure under [`[prompt_injection]`](https://rover-fetch.com/docs/configuration#prompt_injection); full contract in [`rover-fetch.com/docs/mcp-tools`](https://rover-fetch.com/docs/mcp-tools#prompt-injection-guard).
### SSRF protection
Five levels: `strict` · `loopback` · `project` · `lan` · `none`. Every outbound URL is validated twice — once by parsed scheme/host, once against every resolved address before the socket opens — and a **dial-time SSRF resolver** re-applies the policy at each connection attempt, closing the DNS-rebinding TOCTOU window for both the initial request and every redirect hop. Default is `strict` (public IPs, `http`/`https` only). Full level matrix, the always-blocked address floor, and `file://` handling: [`rover-fetch.com/docs/security`](https://rover-fetch.com/docs/security).
### Secret redaction
The tracing layer scrubs URL query-string secrets (`api_key`, `token`, `secret`, `password`) **and** HTTP `Authorization`-style credentials (`Bearer …` / `Basic …`, plus any field literally named `authorization`) before events reach any log destination.
> [!CAUTION]
> The HAR recorder (`[debug] har_path`) writes request/response bodies to disk **unredacted by design** — it's opt-in debug instrumentation. Protect the file with filesystem permissions and treat it as sensitive. Full threat model: [`rover-fetch.com/docs/security`](https://rover-fetch.com/docs/security).
## Features
### Output that respects your token budget
Every fetch returns YAML-frontmattered Markdown with cache provenance, content hash, language, extraction-quality score, and a token estimate. Pass `max_tokens` (MCP) / `--max-tokens` (CLI) and Rover summarises to fit — the body is replaced with a budget-sized summary and the frontmatter gains `summarized: true`. The MCP `fetch` `count_only` arg (and the standalone `count_tokens` tool) returns just the estimate without the body. Token counts span five tokenisers (`cl100k`, `o200k`, `claude`, `llama3`, `qwen3`; default `o200k`).
### Caching, with care
A single SQLite database (WAL mode) backs the cache, task state, and event log. Cache decisions honour `Cache-Control`, `Expires`, `ETag`, `Last-Modified`, and stale-while-revalidate. The default TTL is **15 minutes** — deliberately short, so content that's been poisoned or quietly changed has a small blast radius before the next revalidation.
```sh
rover cache list
rover cache get <url>
rover cache purge 'https://example.com/*'
rover cache stats
rover fetch --force-refresh <url> # bypass cache for this request
```
Cache location: `$XDG_DATA_HOME/rover/rover.db` (or `~/.local/share/rover/rover.db`). Override with `ROVER_DATA_DIR`.
### Background tasks with streaming progress
`batch_fetch` (MCP) and `rover batch <id>` / `rover task <id>` (CLI) schedule long-running work and stream NDJSON events:
```sh
rover batch <id> --monitor # live: item_started, item_done, …, task_completed
rover task <id> # snapshot: progress, ETA, last event
rover task <id> --cancel # cooperative cancellation
rover batch <id> --format=ndjson # single JSON line, scripting-friendly
rover task <id> --monitor --from-event <id> # resume an interrupted stream
```
Tasks survive `rover mcp` restarts: batch jobs resume from persisted progress; summarisation jobs mark `failed` with a clear reason so the agent can re-request.
### Summarisation
Two backends ship by default — and you can configure as many cloud backends as you want, each addressable by name:
```toml
[summarization]
default_backend = "default"
fallback_to_extractive = true
[backends.default]
kind = "extractive" # offline TextRank; no API key, no network
[backends.fast]
kind = "cloud"
provider = "openai" # openai, anthropic, gemini, openai_compat
model = "gpt-4o-mini"
api_key_env = "OPENAI_API_KEY"
```
`openai_compat` covers LM Studio, Ollama, vLLM, and anything else speaking the OpenAI chat-completions dialect. Steering parameters (`focus`, `preserve`, `target_tokens`, `style`) work uniformly across backends. When a cloud backend fails (auth, rate limit, network), Rover transparently falls back to extractive and tags the response with `summarizer_fallback: { from, reason }` — set `fallback_to_extractive = false` for strict-error mode.
### Inline image captioning
Set `images: caption` (MCP) and Rover replaces images with model-written alt-text inline in the Markdown. Captioning uses cloud vision models and is **always compiled in — no feature flag**:
```toml
[image_captions]
default = "openai"
max_per_page = 5
[captioners.openai]
provider = "openai" # openai, anthropic, gemini, openai_compat
model = "gpt-4o-mini"
api_key_env = "OPENAI_API_KEY"
```
`openai_compat` works here too — point it at a local Ollama or LM Studio vision server (e.g. `llama3.2-vision`) for fully offline captioning with no API key.
### Per-domain rate limiting & `robots.txt`
A per-host token bucket and a global concurrency cap, always on and configurable. The `robots.txt` gate is **opt-in** (off by default — Rover is an agent's browser, not a crawler, and robots.txt governs crawling); set `robots.respect = true` to enable it. When enabled, a `Crawl-Delay` floor is respected and the robots cache fails closed (a cached `disallow_all` sentinel for the configured `failure_ttl`), so a flaky robots endpoint doesn't quietly let traffic through.
### HAR debug recording
Set `[debug] har_path` and every round-trip lands in a HAR file that imports cleanly into Chrome DevTools' Network panel. Sub-requests (CSS, fonts, beacons) are excluded so the file stays focused on what Rover actually returned.
```toml
[debug]
har_path = "./rover-debug.har"
har_body_cap = "64KiB"
```
### Optional features (Cargo feature flags)
| Feature | Adds | Notes |
| --- | --- | --- |
| `headless` | JavaScript-rendered SPA support via [`chromiumoxide`](https://github.com/mattsse/chromiumoxide) | Uses system Chrome/Chromium (~32 MB) |
| `local-inference` | Local LLM summarisation via [`mistral.rs`](https://github.com/EricLBuehler/mistral.rs) (default model: Qwen 3.5 0.8B) | ~80 MB; model downloaded on first use |
| `injection-model` | ONNX DeBERTa prompt-injection classifier (guard method 3) | Native ONNX runtime; ~200 MB model downloaded on first use |
```sh
cargo build --release --features headless
cargo build --release --features local-inference,headless
cargo build --release --features injection-model
```
Local models download on first use (or ahead of time via `rover model download <repo_id>`) and live under `$HF_HOME/hub`; manage them with `rover model {list,download,remove}`.
> [!IMPORTANT]
> Cloud captioners (OpenAI, Anthropic, Gemini, OpenAI-compatible) are **always compiled in** — no feature flag. The `headless` feature needs a Chrome/Chromium browser on the host; Rover auto-detects standard install paths (override with `[headless] chrome_executable`), and `rover doctor` verifies the launch path.
Setup details, model recommendations, and memory profiles: [`rover-fetch.com/docs/features`](https://rover-fetch.com/docs/features).
## Configuration
Rover reads `rover.toml` from `$XDG_CONFIG_HOME/rover/rover.toml` (or `~/.config/rover/rover.toml`); override with `ROVER_CONFIG`. Every key has a sensible default — the file is optional.
```sh
rover config show # merged effective config + per-key provenance
rover config set ssrf.level loopback # mutate in place (comments preserved, round-trip validated)
rover config set summarization.default_backend fast
```
A minimal `rover.toml`:
```toml
[fetch]
user_agent = "my-agent/1.0"
timeout_secs = 30
[ssrf]
level = "strict"
[cache]
default_ttl = "15m" # default; raise per-origin Cache-Control still wins
max_ttl = "7d"
[rate_limit]
requests_per_minute_per_domain = 30
per_domain_concurrency = 2
global_concurrency = 8
[summarization]
default_backend = "default"
[backends.default]
kind = "extractive"
```
The full reference — every section, key, and default — lives at [`rover-fetch.com/docs/configuration`](https://rover-fetch.com/docs/configuration).
## Subcommands at a glance
```text
rover fetch <url> one-shot fetch → Markdown on stdout
rover mcp long-running MCP server (stdio)
rover cache list|get|purge|stats inspect / manage the local cache
rover batch <id> batch status; --monitor streams events
rover task <id> task status (any kind); --cancel, --monitor
rover doctor health checks; --format=ndjson for scripting
rover config show|set inspect / mutate config (provenance-aware)
rover model download|list|remove manage local model cache (feature-gated)
```
Full reference, exit codes, and NDJSON event shapes: [`rover-fetch.com/docs/cli`](https://rover-fetch.com/docs/cli).
## Documentation
| Doc | What's in it |
| --- | --- |
| [CLI](https://rover-fetch.com/docs/cli) | Every subcommand, flag, exit code, and NDJSON event shape. |
| [MCP tools](https://rover-fetch.com/docs/mcp-tools) | MCP tool schemas: `fetch`, `batch_fetch`, `summarize`, `get_metadata`, `count_tokens`, and the prompt-injection wire contract. |
| [Configuration](https://rover-fetch.com/docs/configuration) | Every config section and key, with defaults, types, and examples. |
| [Backends](https://rover-fetch.com/docs/backends) | Summarisation backend reference: extractive (TextRank) and cloud providers. |
| [Features](https://rover-fetch.com/docs/features) | Cargo feature flags: `headless`, `local-inference`, `injection-model` — setup, models, sizes. |
| [Security](https://rover-fetch.com/docs/security) | SSRF levels, address floor, DNS-rebinding mitigation, secret redaction, prompt-injection guard, known limitations. |
Contributing: [`CONTRIBUTING.md`](CONTRIBUTING.md) · Security policy: [`SECURITY.md`](SECURITY.md) · Changelog: [`CHANGELOG.md`](CHANGELOG.md).
## License
Licensed under either of [MIT](LICENSE-MIT) or [Apache-2.0](LICENSE-APACHE), at your option.