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ai-first-scraper-mcp

ai-first-scraper-mcp

ai-first-scraper-mcp

Plug Claude Desktop, Cursor, or Cline straight into an ad-free web scraper + search engine. Three tools, one line of config.

PyPI Python MCP License: MIT


What it does

Adds three tools to any MCP-compatible agent:

Tool

What it does

fetch_page

Fetch one URL → return clean Markdown (HTML or PDF).

fetch_pages_batch

Fetch up to 25 URLs in parallel → return Markdown for each.

search_web

Run a web search and return the top-k result pages already converted to Markdown.

No more "the model called curl and then tried to parse 80kB of ad HTML." Your agent receives clean Markdown ready to reason about.

Backed by the ai-first-scraper and ai-first-search APIs.


Related MCP server: CC Web MCP

Install

Fastest — uvx (no install, runs from PyPI on demand)

// claude_desktop_config.json  /  cline_mcp_settings.json  /  ~/.cursor/mcp.json
{
  "mcpServers": {
    "ai-first-scraper": {
      "command": "uvx",
      "args": ["ai-first-scraper-mcp"]
    }
  }
}

Restart your client (Claude Desktop / Cursor / Cline). The three tools above will appear automatically.

Alternative — pip install

pip install ai-first-scraper-mcp
{
  "mcpServers": {
    "ai-first-scraper": {
      "command": "ai-first-scraper-mcp"
    }
  }
}

Where the config file lives

Client

Config path

Claude Desktop (macOS)

~/Library/Application Support/Claude/claude_desktop_config.json

Claude Desktop (Windows)

%APPDATA%\Claude\claude_desktop_config.json

Cursor

~/.cursor/mcp.json

Cline (VS Code)

~/Library/Application Support/Code/User/globalStorage/saoudrizwan.claude-dev/settings/cline_mcp_settings.json


Point at your own backend (optional)

By default this server calls the public ai-first-scraper.onrender.com and ai-first-search.onrender.com instances. If you want to self-host, set env vars in your MCP config:

{
  "mcpServers": {
    "ai-first-scraper": {
      "command": "uvx",
      "args": ["ai-first-scraper-mcp"],
      "env": {
        "SCRAPER_URL": "https://your-scraper.example.com",
        "SEARCH_URL":  "https://your-search.example.com",
        "AFS_TIMEOUT": "60"
      }
    }
  }
}

Verify it works

Open your MCP client and ask the agent:

"Use the search_web tool to find the top 3 recent articles about MCP and summarize them in 5 bullets each."

You should see the agent call search_web, get back Markdown for each result, and produce the summary without ever touching raw HTML.


Companion projects


Develop locally

git clone https://github.com/yubinkim444/ai-first-scraper-mcp.git
cd ai-first-scraper-mcp

uv sync                    # or: pip install -e .
ai-first-scraper-mcp       # speaks MCP over stdio

To test against a local client, point its MCP config at the same command.


About the author

Built by yubinkim444, who also makes Kay's Records — an app for iOS and Android.

If this project saved you time, giving the app a try is the nicest way to say thanks.

License

MIT © yubinkim444

Available Tools

3 tools
fetch_pageA

Fetch a single web page or PDF and return its main content as clean, ad-free Markdown — ready to drop into an LLM prompt.

Args: url: A fully-qualified http(s) URL. max_tokens: Optional soft cap on the returned Markdown (whitespace tokens). When exceeded, the body is truncated and a [...truncated] marker is appended.

Returns: The cleaned Markdown body of the page.

Inputs: url (required), max_tokens. Returns: result

fetch_pages_batchA

Fetch many web pages in parallel and return each one's clean Markdown.

Use this whenever you need to read more than one URL at once — it is far faster than calling fetch_page in a loop because the upstream scraper handles the concurrency.

Args: urls: Up to 25 URLs. max_tokens: Optional per-URL soft cap on the returned Markdown.

Returns: A list of {url, ok, data?, error?} objects in the same order as the input URLs. data is {title, word_count, markdown, links, ...} on success; error contains the failure reason otherwise.

Inputs: urls (required), max_tokens. Returns: result

search_webA

Run a web search and return the top-k result pages already converted to clean Markdown. Use this whenever you need fresh information from the public web — it combines search and read in one call.

Args: q: The user's query (free text). k: How many results to fetch (1–10, default 5). max_tokens: Optional per-result soft cap on the returned Markdown.

Returns: A list of {url, title, snippet, ok, markdown, word_count, error?} result objects. Use title and snippet to decide which results are worth citing, then drop the markdown field into your prompt.

Inputs: q (required), k, max_tokens. Returns: result

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

Loading tool schema changelog…

    TDQS

    A4.5/5.0

    Scored across 3 tools

    Disambiguation5/5

    Each tool has a clearly distinct purpose: single page fetch, batch fetch, and web search. No overlap; agents can easily distinguish them.

    Naming Consistency5/5

    All tool names follow a consistent verb_noun snake_case pattern: fetch_page, fetch_pages_batch, search_web. The slight singularity/plural variation is minor and predictable.

    Tool Count5/5

    Three tools perfectly scope a scraping-focused server: single page, batch, and search. Neither too many nor too few for the domain.

    Completeness5/5

    The tool surface fully covers the core scraping workflow (fetch single, batch, search). No obvious gaps given the stated purpose of returning clean Markdown.

    Maintenance

    ActivityStale
    ResponsivenessNo recent community issues

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