Refinery MCP

Clean raw HTML into LLM-ready text before agents spend tokens.

Quick Install

Automated & IDE Setup

Copy the AI prompt to install this server into Claude Code, Cursor, or another agent — or use 1-click editor setup below.

Add to Cursor Add to VS Code

Not yet automatically verified

We haven't yet run this listing's install command through our automated sandbox check. This isn't a red flag — we're steadily working through the catalog.

Manual Client & Custom JSON ConfigExpand JSON ▾

Client Config & Setup

Choose your client or environment

Target File:~/Library/Application Support/Claude/claude_desktop_config.json

claude_desktop_config.json

{
  "mcpServers": {
    "refinery-mcp": {
      "command": "npx",
      "args": [
        "-y",
        "@larelabs/refinery-mcp"
      ]
    }
  }
}

💡 Paste the JSON block into your client's configuration file under mcpServers, then restart the application.

Capabilities & Tool Schemas (3)Self-reported

Inspect callable tools, capabilities, and parameters exposed to AI agents by Refinery MCP.

clean_url

Fetches a URL through the Refinery Apify Actor and returns dataset rows with clean text and metadata.

clean_html

Cleans raw HTML your agent, crawler, or browser session already fetched.

estimate_savings

Local helper that compares raw HTML vs cleaned text and estimates token savings. This does not call Apify.

Documentation Overview

Refinery MCP

Clean HTML before your agent burns tokens.

Landing page · Apify Actor

Refinery MCP wraps the Refinery Apify Actor 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

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

<html>
  <head>
    <script>gtag("event", "page_view")</script>
    <style>.nav,.cookie,.footer{display:block}</style>
  </head>
  <body>
    <nav>Home · Pricing · Login · Docs · Blog · Careers</nav>
    <aside>Subscribe to our newsletter</aside>
    <article>
      <h1>How ACME cut support ticket routing time by 63%</h1>
      <p>ACME routes 40,000 monthly support tickets through an AI triage system.</p>
      <p>The team reduced retrieval noise by cleaning HTML before chunking.</p>
    </article>
    <footer>Legal · Privacy · Cookie settings · LinkedIn · X</footer>
  </body>
</html>

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

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:

config.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:

config.json

{
  "html": "<html><body><nav>Home Pricing Login</nav><article><h1>Vendor security update</h1><p>We now support SOC 2 exports for enterprise accounts.</p></article><footer>Legal Privacy Careers</footer></body></html>",
  "extractMentions": false,
  "extractHashtags": false
}

Example result:

config.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:

config.json

{
  "raw_chars": 168,
  "clean_chars": 41,
  "estimated_raw_tokens": 42,
  "estimated_clean_tokens": 11,
  "estimated_token_savings": 31,
  "reduction_pct": 76
}

Install

Terminal

npx -y @larelabs/refinery-mcp

Set your Apify token:

server.ts

export APIFY_TOKEN=apify_api_xxx
export REFINERY_ACTOR_ID=larelabs/refinery-html-to-llm-cleaner

Cursor / Claude Desktop config

Use the published package:

config.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

config.json

{
  "mcpServers": {
    "refinery": {
      "command": "npm",
      "args": ["run", "dev", "--prefix", "/absolute/path/to/refinery-mcp"],
      "env": {
        "APIFY_TOKEN": "apify_api_xxx"
      }
    }
  }
}

Smoke Test

Terminal

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

Read the full README →View source on GitHub →

Related MCP Servers

View all in Developer Tools View all alternatives

Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks — not a rating.

npm downloads

224

Package downloads in the last 30 days.

Last commit

2mo ago

Most recent push to the default branch.

Tools exposed

Callable tools this server registers over MCP.

Reviews

No reviews yet — be the first to share how this listing worked for you.

Frequently Asked Questions about Refinery MCP

Add the following block to your claude_desktop_config.json under mcpServers: "mcpServers": { "refinery-mcp": { "command": "npx", "args": ["-y","@larelabs/refinery-mcp"] } }

AllMCPs Directory Badge

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Showcase your server listing on GitHub or your project documentation. Embed this dynamic SVG badge to highlight official listing status and live engagement.

Badge Style:

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Markdown (GitHub README)

[![AllMCPs](https://allmcps.com/api/badge/refinery-mcp?style=directory)](https://allmcps.com/mcp/refinery-mcp)

HTML Embed

<a href="https://allmcps.com/mcp/refinery-mcp"><img src="https://allmcps.com/api/badge/refinery-mcp?style=directory" alt="Refinery MCP on AllMCPs" /></a>

Technical Specs & Signals

CategoryDeveloper Tools

11/13 checks healthy over the last 45d

51Quality signal: Good · 51/100How this signal is calculated ▾

Server availabilityNot measured

Not scored for repo-hosted servers — we can't reach the running server, only its GitHub page. Hosted MCP endpoints are health-checked live.

Verified ownership10/20

Documentation & tools23/30

Adoption & activity5/15

Community engagement0/10

A guidance signal from public completeness & health data — not a user rating. New listings start lower and rise as they add docs, get verified, and grow adoption. Signals we can't observe for a listing are skipped, not counted against it.

Supply-chain signal

No high-severity advisories surfaced by our automated scan.

Critical 0High 0Medium 0Low 0

Scanned 1d ago via OSV.dev · @larelabs/refinery-mcp (npm)

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More in 💻 Developer Tools →Best MCP servers for Developers →Alternatives to Refinery MCP →Install in Claude Desktop Install in Cursor Install in VS Code Setup guides for all 13 MCP clients

Refinery MCP

Clean raw HTML into LLM-ready text before agents spend tokens.

Quick Install

Automated & IDE Setup

Copy the AI prompt to install this server into Claude Code, Cursor, or another agent — or use 1-click editor setup below.

Add to Cursor Add to VS Code

Not yet automatically verified

We haven't yet run this listing's install command through our automated sandbox check. This isn't a red flag — we're steadily working through the catalog.

Manual Client & Custom JSON ConfigExpand JSON ▾

Client Config & Setup

Choose your client or environment

Target File:~/Library/Application Support/Claude/claude_desktop_config.json

claude_desktop_config.json

{
  "mcpServers": {
    "refinery-mcp": {
      "command": "npx",
      "args": [
        "-y",
        "@larelabs/refinery-mcp"
      ]
    }
  }
}

💡 Paste the JSON block into your client's configuration file under mcpServers, then restart the application.

Capabilities & Tool Schemas (3)Self-reported

Inspect callable tools, capabilities, and parameters exposed to AI agents by Refinery MCP.

clean_url

Fetches a URL through the Refinery Apify Actor and returns dataset rows with clean text and metadata.

clean_html

Cleans raw HTML your agent, crawler, or browser session already fetched.

estimate_savings

Local helper that compares raw HTML vs cleaned text and estimates token savings. This does not call Apify.

Documentation Overview

Refinery MCP

Clean HTML before your agent burns tokens.

Landing page · Apify Actor

Refinery MCP wraps the Refinery Apify Actor 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

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

<html>
  <head>
    <script>gtag("event", "page_view")</script>
    <style>.nav,.cookie,.footer{display:block}</style>
  </head>
  <body>
    <nav>Home · Pricing · Login · Docs · Blog · Careers</nav>
    <aside>Subscribe to our newsletter</aside>
    <article>
      <h1>How ACME cut support ticket routing time by 63%</h1>
      <p>ACME routes 40,000 monthly support tickets through an AI triage system.</p>
      <p>The team reduced retrieval noise by cleaning HTML before chunking.</p>
    </article>
    <footer>Legal · Privacy · Cookie settings · LinkedIn · X</footer>
  </body>
</html>

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

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

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:

config.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:

config.json

{
  "html": "<html><body><nav>Home Pricing Login</nav><article><h1>Vendor security update</h1><p>We now support SOC 2 exports for enterprise accounts.</p></article><footer>Legal Privacy Careers</footer></body></html>",
  "extractMentions": false,
  "extractHashtags": false
}

Example result:

config.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:

config.json

{
  "raw_chars": 168,
  "clean_chars": 41,
  "estimated_raw_tokens": 42,
  "estimated_clean_tokens": 11,
  "estimated_token_savings": 31,
  "reduction_pct": 76
}

Install

Terminal

npx -y @larelabs/refinery-mcp

Set your Apify token:

server.ts

export APIFY_TOKEN=apify_api_xxx
export REFINERY_ACTOR_ID=larelabs/refinery-html-to-llm-cleaner

Cursor / Claude Desktop config

Use the published package:

config.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

config.json

{
  "mcpServers": {
    "refinery": {
      "command": "npm",
      "args": ["run", "dev", "--prefix", "/absolute/path/to/refinery-mcp"],
      "env": {
        "APIFY_TOKEN": "apify_api_xxx"
      }
    }
  }
}

Smoke Test

Terminal

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

Read the full README →View source on GitHub →

Related MCP Servers

View all in Developer Tools View all alternatives

Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks — not a rating.

npm downloads

224

Package downloads in the last 30 days.

Last commit

2mo ago

Most recent push to the default branch.

Tools exposed

Callable tools this server registers over MCP.

Reviews

No reviews yet — be the first to share how this listing worked for you.

Frequently Asked Questions about Refinery MCP

Add the following block to your claude_desktop_config.json under mcpServers: "mcpServers": { "refinery-mcp": { "command": "npx", "args": ["-y","@larelabs/refinery-mcp"] } }

AllMCPs Directory Badge

Full Badge Customizer

Showcase your server listing on GitHub or your project documentation. Embed this dynamic SVG badge to highlight official listing status and live engagement.

Badge Style:

Live Dynamic SVG Preview

Markdown (GitHub README)

[![AllMCPs](https://allmcps.com/api/badge/refinery-mcp?style=directory)](https://allmcps.com/mcp/refinery-mcp)

HTML Embed

<a href="https://allmcps.com/mcp/refinery-mcp"><img src="https://allmcps.com/api/badge/refinery-mcp?style=directory" alt="Refinery MCP on AllMCPs" /></a>

Technical Specs & Signals

CategoryDeveloper Tools

11/13 checks healthy over the last 45d

51Quality signal: Good · 51/100How this signal is calculated ▾

Server availabilityNot measured

Not scored for repo-hosted servers — we can't reach the running server, only its GitHub page. Hosted MCP endpoints are health-checked live.

Verified ownership10/20

Documentation & tools23/30

Adoption & activity5/15

Community engagement0/10

Supply-chain signal

No high-severity advisories surfaced by our automated scan.

Critical 0High 0Medium 0Low 0

Scanned 1d ago via OSV.dev · @larelabs/refinery-mcp (npm)

★ Featured

AllMCPs Server

Explore Server →

Own this project?

Free dofollow backlink: add your website and place the AllMCPs badge on it — no claim needed. We detect it automatically and keep it verified as long as the badge stays live.

Claim & get free dofollow

Share & Embed

Add our SVG badge (dark/light directory styles) or embeddable widget to your site.

Explore more

More in 💻 Developer Tools →Best MCP servers for Developers →Alternatives to Refinery MCP →Install in Claude Desktop Install in Cursor Install in VS Code Setup guides for all 13 MCP clients