WebShift

---

What is WebShift

WebShift is a Rust library and MCP server that shifts noisy web pages into clean, right-sized text for LLM consumption.

Raw HTML is mostly junk: scripts, ads, navigation menus, cookie banners, tracking pixels. Feeding it directly to an LLM floods the context window with tens of thousands of useless tokens and leaves no room for reasoning. WebShift strips all that noise, sterilizes the text, and enforces strict size budgets so the model receives only the content that matters.

What you get

Depending on the features you enable, WebShift can be four things:

Use case	Crate	Feature flags	What it does
HTML denoiser	webshift	default-features = false	clean() — pure Rust HTML-to-text pipeline. Strips noise elements, sterilizes Unicode/BiDi, collapses whitespace. Zero network, zero config. Drop into any Rust project that processes web content for LLMs.
HTML text rewriter	webshift	features = ["text-map"]	extract_text_nodes() + replace_text_nodes() — extract individual text nodes from HTML, manipulate them (translate, rewrite, simplify), and rebuild the HTML with structure intact. Tags, attributes, and links are never touched.
Web content client	webshift	default or features = ["llm"]	fetch() + query() — streaming HTTP fetcher with size caps, 8 search backends, BM25 reranking, optional LLM query expansion and summarization. Full pipeline from search query to structured results.
MCP server	webshift-mcp	all features	Native binary (mcp-webshift) that exposes webshift_query, webshift_fetch, and webshift_onboarding over MCP stdio. Single static binary, zero runtime dependencies.

When to use WebShift

• You're building an AI agent that needs web search and you want clean, budget-controlled text — not raw HTML.
• You're processing web pages in a Rust pipeline and need a reliable HTML-to-text cleaner that strips noise without losing real content.
• You need an LLM to translate, rewrite, or simplify text inside HTML without corrupting the markup — text-map gives you a safe round-trip.
• You want an MCP web search server that works as a single binary — no Python, no pip, no venv, no Docker (unless you want it).
• You need hard guarantees on output size: per-page caps, total budget caps, streaming download limits.

When NOT to use WebShift

• You need a headless browser that renders JavaScript-heavy SPAs. WebShift parses static HTML — it doesn't execute JS.
• You need to render or screenshot a page preserving its visual layout. WebShift processes HTML structure but does not render CSS or compute layout. (Note: text-map does preserve the DOM structure for text rewriting use cases.)
• You're building a web scraper that needs to navigate across pages, fill forms, or handle authentication flows.

---

How it works

Question
  |
  +- (optional) LLM query expansion -> multiple search variants
  |
  +- Search via backend (SearXNG, Brave, Tavily, Exa, SerpAPI, Google, Bing, HTTP)
  |
  +- Deduplicate + filter binary URLs
  |
  +- Streaming fetch with per-page size cap
  |
  +- HTML cleaning -> plain text (noise elements, scripts, nav removed)
  |
  +- Unicode/BiDi sterilization
  |
  +- BM25 deterministic reranking
  |   +- (optional) LLM-assisted tier-2 reranking
  |
  +- Budget-aware truncation across all sources
  |
  +- (optional) LLM Markdown summary with inline citations
  |
  +- Structured JSON output

For a detailed explanation of each pipeline stage, BM25 parameters, adaptive budget allocation, and real compression metrics see Under the Hood. For the full configuration reference (TOML, env vars, CLI args) see Configuration. For ready-to-use examples see Use Cases.

---

Installation

Binary (MCP server)

cargo install webshift-mcp

The binary is called mcp-webshift.

From source

cargo install --path crates/webshift-mcp

As a library

# Full pipeline (search + fetch + clean + rerank)
webshift = "0.2"

# Cleaner + fetcher only (no search backends)
webshift = { version = "0.2", default-features = false }

# Text-map only (extract/replace text nodes in HTML)
webshift = { version = "0.2", default-features = false, features = ["text-map"] }

# Everything including LLM features
webshift = { version = "0.2", features = ["llm"] }

---

Quick start

1. Set up a search backend

The easiest option is SearXNG — free, self-hosted, no API key:

docker run -d -p 8080:8080 searxng/searxng

No Docker? Use a cloud backend — see Search backends.

2. Configure your MCP client

{
  "mcpServers": {
    "webshift": {
      "command": "mcp-webshift",
      "args": ["--default-backend", "searxng"]
    }
  }
}

That's it. The agent now has webshift_query, webshift_fetch, and webshift_onboarding.

For client-specific setup see docs/integrations/.

---

MCP tools

Tool	Description
webshift_query	Full search pipeline: search + fetch + clean + rerank + (optional) summarize
webshift_fetch	Single page fetch and clean
webshift_onboarding	Returns a JSON guide for the agent (budgets, backends, tips)

webshift_query parameters

Parameter	Type	Default	Description
queries	string or list	required	Search query or list of queries
num_results_per_query	integer	5	Results per query
lang	string	none	Language filter (e.g. "en")
backend	string	config default	Override search backend

---

Configuration

Resolution order (highest priority first):

1. CLI args — --default-backend, --brave-api-key, etc.
2. Environment variables — WEBSHIFT_* prefix
3. Config file — webshift.toml (current dir, then ~/webshift.toml)
4. Built-in defaults

Config file

[server]
max_query_budget    = 32000   # total char budget across all sources
max_result_length   = 8000    # per-page char cap
max_total_results   = 20      # hard cap on results per call
max_download_mb     = 1       # streaming cap per page (MB)
search_timeout      = 8       # seconds
results_per_query   = 5
oversampling_factor = 2
adaptive_budget     = "auto"  # "auto" | "on" | "off" — budget allocation mode

[backends]
default = "searxng"

[backends.searxng]
url = "http://localhost:8080"

[backends.brave]
api_key = "BSA-..."

[backends.tavily]
api_key = "tvly-..."

[backends.exa]
api_key = "..."

[backends.serpapi]
api_key = "..."
engine  = "google"    # google | bing | duckduckgo | yandex

[backends.google]
api_key = "..."
cx      = "..."       # Custom Search Engine ID

[backends.bing]
api_key = "..."
market  = "en-US"

[backends.http]
url           = "https://my-search.example.com/api/search"
query_param   = "q"
count_param   = "limit"
results_path  = "data.items"     # dot-path to results array in JSON response
title_field   = "title"
url_field     = "link"
snippet_field = "description"

[backends.http.headers]
"Authorization" = "Bearer my-token"

[llm]
enabled               = false
base_url              = "http://localhost:11434/v1"   # OpenAI-compatible
api_key               = ""
model                 = "gemma3:27b"
timeout               = 60
expansion_enabled     = true
summarization_enabled = true
llm_rerank_enabled    = false

For every setting with all three config methods (TOML, env vars, CLI args) and plain-language descriptions, see the full Configuration Reference. Ready-to-use config examples are in Use Cases and examples/.

Key environment variables

WEBSHIFT_DEFAULT_BACKEND=searxng
WEBSHIFT_SEARXNG_URL=http://localhost:8080
WEBSHIFT_BRAVE_API_KEY=BSA-xxx
WEBSHIFT_GOOGLE_API_KEY=xxx
WEBSHIFT_GOOGLE_CX=xxx
WEBSHIFT_BING_API_KEY=xxx
WEBSHIFT_LLM_ENABLED=true
WEBSHIFT_LLM_BASE_URL=http://localhost:11434/v1
WEBSHIFT_LLM_MODEL=gemma3:27b

---

Search backends

Backend	Auth	Notes
SearXNG	none	Self-hosted, free. Default: http://localhost:8080
Brave	API key	Free tier. brave.com/search/api
Tavily	API key	AI-oriented. tavily.com
Exa	API key	Neural search. exa.ai
SerpAPI	API key	Multi-engine proxy (Google, Bing, DDG...). serpapi.com
Google	API key + CX	Custom Search. Free: 100 req/day. programmablesearchengine.google.com
Bing	API key	Web Search API. Free: 1,000 req/month. Microsoft Azure
HTTP	configurable	Generic REST backend — no code required, TOML-only config

---

LLM features (optional)

All opt-in — disabled by default, no data leaves your machine unless enabled.

Feature	What it does
Query expansion	Single query -> N complementary search variants
Summarization	Markdown report with inline [1] [2] citations
LLM reranking	Tier-2 reranking on top of deterministic BM25

Cross-language normalization (bonus): when BM25 reranking surfaces pages in foreign languages (e.g. Chinese, Japanese, Arabic), the LLM summarizer still produces the final report in the prompt language. The agent receives clean, readable output regardless of the language mix in the source pages.

Works with any OpenAI-compatible API (OpenAI, Ollama, vLLM, LM Studio, etc.):

[llm]
enabled  = true
base_url = "http://localhost:11434/v1"
model    = "gemma3:27b"

---

Anti-flooding protections

Always active — the core value proposition:

Protection	Description
max_download_mb	Streaming cap — never buffers full response
max_result_length	Hard cap on characters per cleaned page
max_query_budget	Total character budget across all sources
max_total_results	Hard cap on results per call
Binary filter	.pdf, .zip, .exe, etc. filtered before any network request
Unicode sterilization	BiDi control chars, zero-width chars removed

---

Library usage

use webshift::{Config, clean, fetch, query};

// Clean raw HTML — cap output at 8000 chars
let result = clean("<html><body><p>Hello world</p></body></html>", 8000);
println!("{}", result.text);

// Pass 0 to disable the per-page cap entirely (no truncation)
let full = clean("<html><body><p>Hello world</p></body></html>", 0);
assert!(!full.truncated);

// Fetch and clean a single page
let config = Config::default();
let page = fetch("https://example.com", &config).await?;

// Full search pipeline
let results = query(&["rust async programming"], &config).await?;
for source in &results.sources {
    println!("[{}] {} — {} chars", source.id, source.title, source.content.len());
}

Text-map: rewrite HTML content without breaking markup

Extract text nodes, manipulate them (translate, rewrite, simplify), and rebuild the HTML with structure, attributes, and links intact.

use webshift::{extract_text_nodes, replace_text_nodes, TextReplacement};

let html = r#"<p>Hello <a href="https://example.com">world</a></p>"#;
let map = extract_text_nodes(html);
// map.nodes = [(0, "Hello"), (1, "world")]

let replacements = vec![
    TextReplacement { id: 0, text: "Ciao".into() },
    TextReplacement { id: 1, text: "mondo".into() },
];
let result = replace_text_nodes(html, &replacements).unwrap();
// → <p>Ciao <a href="https://example.com">mondo</a></p>
// href untouched, tags intact, only text changed.

Requires features = ["text-map"]. See Use Cases #11 for a full translation example.

Feature flags

Feature	Default	Enables
backends	on	All search backends + query pipeline
llm	off	LLM client, expander, summarizer, LLM reranking
text-map	off	extract_text_nodes() + replace_text_nodes() — DOM round-trip for content rewriting

---

Integrations

Platform	Guide
Claude Desktop, Claude Code, Cursor, Windsurf, VS Code	IDE Integration
Zed — native extension with auto-download and Configure Server modal	Zed Extension
Gemini CLI, Claude CLI, custom agents	Agent Integration

---

Beta Status

WebShift is in beta. Core functionality is stable and the server is used daily, but the API surface may still change before 1.0.

Feedback is very welcome. If something doesn't work as expected, behaves oddly, or you have a use case that isn't covered:

Open an issue on GitHub

Bug reports, configuration questions, and feature requests all help shape the roadmap.

Contributing

Contributions are welcome! Please see CONTRIBUTING.md for detailed guidelines on:

• Development setup and workflow
• Code style and conventions
• Testing requirements
• Documentation standards
• Pull request process

License

MIT License — see LICENSE for details.

Links

• GitHub Repository — Source code and issues
• Docs.rs — API documentation
• MCP Registry — WebShift on Model Context Protocol Registry
• MCP Protocol — Model Context Protocol specification

---

Need help? Check the documentation or open an issue on GitHub.