Dompruner

MCP server that cuts web page token cost by 97–99% via DOM AST extraction — no API key, no Vector DB, no embedding API required.

DomPruner fetches a URL, parses the DOM as an Abstract Syntax Tree, prunes noise subtrees (nav, ads, scripts, footers) by FQN path, and returns compact Markdown. Every response includes a token stats header so you can see the reduction at a glance.

> [DomPruner] stripe.com
> | Raw HTML  | 305,778 tokens |
> | Refined   |     988 tokens |
> | Reduction |        99.7%   |
> Fetch: 1,546ms · Parse: 8.2ms

---

Quick Start

No installation, no API key:

npx -y dompruner-mcp

Claude Code

Add to .mcp.json in your project root (or ~/.claude/.mcp.json for global):

{
  "mcpServers": {
    "dompruner": {
      "type": "stdio",
      "command": "npx",
      "args": ["-y", "dompruner-mcp"]
    }
  }
}

Run /mcp in Claude Code to verify — you should see dompruner with dompruner_fetch and dompruner_analyze listed.

Claude Desktop

Edit ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or %APPDATA%\Claude\claude_desktop_config.json (Windows):

{
  "mcpServers": {
    "dompruner": {
      "command": "npx",
      "args": ["-y", "dompruner-mcp"]
    }
  }
}

Restart Claude Desktop. Tools appear in the tool picker automatically.

Cursor / Windsurf / other MCP clients

Add the same mcpServers block to your client's MCP config file (.cursor/mcp.json, etc.):

{
  "mcpServers": {
    "dompruner": {
      "type": "stdio",
      "command": "npx",
      "args": ["-y", "dompruner-mcp"]
    }
  }
}

Environment Variables

Variable	Required	Description
BRAVE_API_KEY	No	Enables dompruner_search via Brave Search API. Falls back to DuckDuckGo HTML scraping if unset.

No other environment variables are needed.

---

Tools

dompruner_fetch

Fetch a URL and return DOM-refined Markdown.

dompruner_fetch(url?, query?)

Parameter	Type	Required	Description
url	string	No*	Page to fetch and refine
query	string	No	Search intent — enables BM25+ section filtering

*If url is omitted and only query is provided, DomPruner requests URL resolution from the host LLM via MCP sampling (createMessage). If the client does not support sampling, a guide is returned asking the LLM to search first and call dompruner_fetch again with the resolved URL.

Response format:

> [DomPruner] fastapi.tiangolo.com
> |           | Tokens |
> |-----------|--------|
> | Raw HTML  | 35,905 |
> | Refined   |  2,011 |
> | Reduction |  94.4% |
> Fetch: 83ms · Parse: 20.7ms

# First Steps

The simplest FastAPI file could look like this:
...

dompruner_analyze

Returns a token-reduction report and Semantic Anchor list without the full page content — useful for auditing a page before retrieval.

dompruner_analyze(url)

Reports: render type (SSR / SSG / CSR), raw vs refined token counts, reduction %, and the list of headings and meta anchors found.

dompruner_workflow prompt

The server registers an dompruner_workflow prompt that is delivered to the host LLM on connection. It sets the correct usage pattern:

• URL known → call dompruner_fetch(url) directly
• URL unknown → use native web search to resolve the URL, then call dompruner_fetch(url)

DomPruner handles content refinement; URL discovery is the host LLM's job.

---

How It Works

URL
 └─▶ fetchPage()       — tiered fetch: direct → UA rotation → Playwright fallback
      │
      ├─▶ [SSG]  extractSsgMarkdown()
      │           walks __NEXT_DATA__ RSC tree → clean Markdown  (≥ 90% reduction)
      │           skips DOM parse entirely
      │
      └─▶ [SSR/CSR]  pre-strip <script>/<style>/<svg>  (80–92% size ↓)
                └─▶ parse5 DOM Tree
                     └─▶ FQN Router (L1)        keeps p / h1–h5 / li / pre / code
                          │                      prunes nav / footer / aside / form
                          └─▶ Heading Cluster (L2)  dev-doc structure detection
                               └─▶ CETD Engine (L3)  text-density scoring fallback
                                    └─▶ BM25+ Section Filter  query-aware ranking
                                         └─▶ Semantic Anchor  heading hierarchy + meta
                                              └─▶ Compact Markdown  ──▶  LLM context

Render Type Detection

Type	Signal	Strategy
SSG	__NEXT_DATA__, window.__NUXT__, window.page	RSC tree walk — DOM parse skipped
SSR	Body text density ≥ 2%	Full DOM AST pipeline (L1→L2→L3)
CSR	Body text density < 2%	DOM AST pipeline (partial content)

Tiered Fetch

Plain HTTP fails silently on many documentation sites (HTTP 403, UA blocks, JS-gated content). DomPruner runs three tiers before giving up:

Level	Trigger	Method
L1	Default	Native fetch
L2	403 / 429 response	User-Agent rotation (3 browser UA strings)
L3	CSR detected or L2 fails	playwright-core headless browser

playwright-core is an optional peer dependency — install it only if you need L3:

npm install playwright-core
npx playwright install chromium

BM25+ Section Filter

When query is provided, the extracted sections are ranked by BM25+ score. Two weighting adjustments are applied:

• Heading boost (2.5×) — sections under a relevant heading rank higher, suppressing sidebar noise
• Depth decay (0.4) — deeply nested sections score lower than top-level content
• Ancestor preservation — parent headings of selected sections are always included for context

Result: only the most relevant sections enter the LLM context, within a configurable token budget.

---

Benchmark

Tested on 9 real-world sites across 4 categories. All numbers from live fetches.

Token estimation: Korean ÷ 2, others ÷ 4 chars per token.

Site	Category	Raw HTML	Chunk RAG	DomPruner	DomPruner+BM25	Reduction
Stripe API	API Docs	305,767	1,255	1,495	988	99.7%
Anthropic API	API Docs	236,773	1,255	2,255	979	99.6%
GitHub REST	API Docs	71,434	1,255	500	500	99.3%
TypeScript Handbook	Language	47,334	1,180	303	303	99.4%
MDN Fetch API	Language	38,087	1,194	726	726	98.1%
React useState	Framework	110,966	1,255	5,491	1,158	99.0%
FastAPI	Framework	38,300	1,255	3,436	1,029	97.3%
Wikipedia REST	General	44,816	1,255	3,283	1,158	97.4%
Wikipedia AST	General	44,551	1,255	2,758	1,106	97.5%
AVERAGE			1,240	2,250	883	98.6%

DomPruner+BM25 delivers 29% fewer tokens than Chunk RAG on average.

vs Chunk RAG

	Chunk RAG	DomPruner+BM25
Avg output tokens	~1,240	~883 (29% less)
Token reduction vs raw HTML	~99%	~99%
Heading / structure preservation	Query-dependent	Consistent
Extra infrastructure	Embedding API + Vector DB	None
Query required upfront	Yes	No (optional)
Processing overhead	~100 ms+ (embed API)	~1 ms
SSG sites (Next.js / Nuxt)	DOM scrape	RSC tree walk
Context breaks at chunk boundaries	Yes	No

---

Architecture

src/
  mcp-server.ts          — MCP stdio transport + tool/prompt handlers
  pipeline.ts            — Orchestrator: fetch → parse → extract → rules → serialize
                           Includes 5-min TTL URL cache
  ast/
    fetcher.ts           — Tiered HTTP fetch (native → UA rotation → Playwright)
    parser.ts            — Pre-strip (<script>/<style>/<svg>) + parse5 DOM builder
    core-extractor.ts    — L1→L2→L3 extraction cascade
    fqn-router.ts        — L1: FQN semantic selector matching + noise pruning
    heading-cluster.ts   — L2: heading-block clustering for developer docs
    cetd.ts              — L3: Content/Tag-Density scoring fallback
    ssg-extractor.ts     — Next.js / Nuxt / Gatsby __NEXT_DATA__ RSC tree walk
    anchor.ts            — Semantic anchor extraction (title, meta description, h1–h3)
  middleware/
    serializer.ts        — FQNNode[] → Compact Markdown + token estimator
  rule-engine/
    registry.ts          — URL pattern → Rule set resolution and chaining
    types.ts             — Rule interface definitions
    builtin/
      section-bm25.ts    — BM25+ with heading boost + ancestor preservation
      http-endpoint.ts   — HTTP method/path/response block reformatter
      code-signature.ts  — Code block function/class signature extractor

---

Development

git clone https://github.com/dong7812/AST-RAG-MCP.git
cd AST-RAG-MCP
npm install
npm run dev    # tsx watch — no build step needed
npm run build  # tsc → dist/

To test a tool call locally:

echo '{"jsonrpc":"2.0","id":1,"method":"tools/call","params":{"name":"dompruner_fetch","arguments":{"url":"https://fastapi.tiangolo.com","query":"routing"}}}' \
  | npm run dev 2>/dev/null

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Roadmap

• #1 — PDF & Office file extraction via Content-Type routing
• #2 — BFS site crawl via dompruner_crawl MCP tool + sitemap.xml support
• #3 — Image content extraction (local OCR / opt-in VLM captioning)
• #4 — Structured JSON output mode (deterministic HTML extraction + opt-in LLM schema)

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License

MIT