# Refinery MCP **Category:** 💻 Developer Tools **Repository:** https://github.com/LareLabs/refinery-mcp **Views:** 0 **Installs:** 0 **Upvotes:** 0 **Directory Page:** https://allmcps.com/mcp/refinery-mcp ## Description Clean raw HTML into LLM-ready text before agents spend tokens. ## Claude Desktop Quick Installation Heuristic fallback — verify the package name and runner against the repository README before running it. Uses `npx` (confidence: low): ```json "mcpServers": { "refinery-mcp": { "command": "npx", "args": ["-y","refinery-mcp"] } } ``` ## Documentation & README # Refinery MCP Clean HTML before your agent burns tokens. [Landing page](https://larelabs.github.io/refinery-mcp/) · [Apify Actor](https://apify.com/larelabs/refinery-html-to-llm-cleaner) Refinery MCP wraps the [Refinery Apify Actor](https://apify.com/larelabs/refinery-html-to-llm-cleaner) as an MCP server so Claude, Cursor, and other agents can turn raw HTML or URLs into clean LLM-ready text plus `word_count`. ![Agent pipeline: fetch, Refinery MCP, clean text, RAG](https://i.imgur.com/rKLRTc1.png) ```mermaid flowchart LR A[Agent needs web context] --> B[Fetch URL or raw HTML] B --> C[Refinery MCP] C --> D[Refinery Apify Actor] D --> E[Clean text + word_count] E --> F[RAG / embeddings / LLM context] ``` ## The Problem Agents are getting good at fetching web pages. The problem is what they fetch: ```html

How ACME cut support ticket routing time by 63%

ACME routes 40,000 monthly support tickets through an AI triage system.

The team reduced retrieval noise by cleaning HTML before chunking.

``` The model does not need most of that. It needs this: ```text How ACME cut support ticket routing time by 63% ACME routes 40,000 monthly support tickets through an AI triage system. The team reduced retrieval noise by cleaning HTML before chunking. ``` ![Before and after: bloated HTML vs clean LLM-ready text and token savings](https://i.imgur.com/RxmHFBz.png) Refinery MCP gives your agent a tool for that middle step: ```text fetch page -> refine HTML -> send clean text to RAG / embeddings / LLM ``` ## Why Agents can fetch pages, but raw HTML is noisy and expensive: - scripts, styles, tracking tags - nav, footers, cookie banners - repeated links and layout markup - huge token burn before the model sees the real content Refinery is the middle step your agent can call before it stuffs web context into a prompt: ```text fetch/render -> clean/refine -> chunk/embed/answer ``` It is **not a crawler**. Use Firecrawl, Crawl4AI, Playwright, browser automation, or your own fetcher when you need rendering. Use Refinery when you already have a URL or raw HTML and want a cheap cleanup pass before the LLM. ## When To Use It Use Refinery MCP when: - your agent already fetched a page but got bloated HTML - you want a deterministic cleanup step before RAG ingestion - you need `word_count` / token-ish savings before embedding - you want to separate crawling from content cleanup Do not use it as your browser renderer, anti-bot layer, or site crawler. ## Tools ### `clean_url` Fetches a URL through the Refinery Apify Actor and returns dataset rows with clean text and metadata. Example input: ```json { "url": "https://docs.stripe.com/payments", "removeScripts": true, "removeStyles": true } ``` ### `clean_html` Cleans raw HTML your agent, crawler, or browser session already fetched. Example input: ```json { "html": "

Vendor security update

We now support SOC 2 exports for enterprise accounts.

", "extractMentions": false, "extractHashtags": false } ``` Example result: ```json { "text": "Vendor security update\n\nWe now support SOC 2 exports for enterprise accounts.", "word_count": 10, "content_type": "web", "language": "en", "processing_time_ms": 44.96, "success": true } ``` ### `estimate_savings` Local helper that compares raw HTML vs cleaned text and estimates token savings. This does not call Apify. Example output: ```json { "raw_chars": 168, "clean_chars": 41, "estimated_raw_tokens": 42, "estimated_clean_tokens": 11, "estimated_token_savings": 31, "reduction_pct": 76 } ``` ## Install ```bash npx -y @larelabs/refinery-mcp ``` Set your Apify token: ```bash export APIFY_TOKEN=apify_api_xxx export REFINERY_ACTOR_ID=larelabs/refinery-html-to-llm-cleaner ``` ## Cursor / Claude Desktop config Use the published package: ```json { "mcpServers": { "refinery": { "command": "npx", "args": ["-y", "@larelabs/refinery-mcp"], "env": { "APIFY_TOKEN": "apify_api_xxx", "REFINERY_ACTOR_ID": "larelabs/refinery-html-to-llm-cleaner" } } } } ``` Or run from source during development: ```bash git clone https://github.com/LareLabs/refinery-mcp cd refinery-mcp npm install npm run build ``` ```json { "mcpServers": { "refinery": { "command": "npm", "args": ["run", "dev", "--prefix", "/absolute/path/to/refinery-mcp"], "env": { "APIFY_TOKEN": "apify_api_xxx" } } } } ``` ## Smoke Test ```bash npm run build APIFY_TOKEN=apify_api_xxx npm run smoke ``` The smoke test starts the MCP server over stdio, lists tools, and calls `estimate_savings` without spending Apify credits. ## Example Agent Prompt ```text Use Refinery MCP to clean this docs page before summarizing it: https://docs.stripe.com/payments Return the clean text, word_count, and a short summary. Do not summarize raw HTML. ``` Another useful prompt: ```text I fetched this page HTML with Playwright. Use Refinery MCP clean_html before adding it to my RAG ingestion queue. Return the cleaned text and estimated token savings. ``` ## Roadmap - Glama listing (`glama.json` added — submit at https://glama.ai/mcp/servers) - mcp.so directory PR (pending) - Hosted HTTP/SSE MCP transport - Batch URL cleanup tool - Glama / PulseMCP / FindMCP / mcp.so listings - Optional direct REST wrapper for RapidAPI - Token savings benchmark page ## License MIT