We Gave a Local LLM Live Web Access. Here’s How

This article was originally published on the Zenrows blog.

We gave a local LLM live web access, and this post shows how. You'll connect a qwen3 model in Ollama to Zenrows MCP, first with no code through the ollmcp bridge and then with a short Python agent that is its own MCP client. You'll need Ollama, a tool-capable model, Node.js, and Python 3.11 or later. A Zenrows API key is optional.

Ollama publishes web_search and web_fetch as hosted APIs that you call with an Ollama API key, and it has no MCP client of its own. In our test runs, the local qwen3 agent read a Cloudflare-fronted Indeed search and cleared an anti-bot challenge page through Zenrows MCP. A plain requests.get() to the same challenge page returned a 403. The code is on GitHub.

You ask a question, Ollama picks a tool, Zenrows makes the requests, and the target site returns the pages. The model never contacts the site. The agent calls the extract tool, Zenrows fetches the page through Fetch with protected access, JavaScript rendering, and proxy rotation in one API call, and the model gets the page as structured JSON fields.

Zenrows MCP has two transports. The remote server runs over Streamable HTTP and authenticates with your API key as a Bearer token. The local server runs as a STDIO subprocess through the @zenrows/mcp npm package with your key in an environment variable. This tutorial uses the local one, since it runs next to Ollama on your machine.

These tools matter for this setup.

scrape returns a page as Markdown by default, with HTML, PDF, and screenshot output also available. extract returns structured JSON, which matters for local models with small context windows. browser_* tools open a cloud browser session for tasks that span more than one request. crawl_* tools collect the URLs behind one start page. batch_* tools submit a list of URLs as one background job.

brew install ollama
brew services start ollama
ollama pull qwen3
ollama show qwen3:latest

Ollama registers the model as qwen3:latest. The ollama show output lists tools under Capabilities, which confirms the model can call the Zenrows tools.

  Model
    architecture        qwen3
    parameters          8.2B
    context length      40960
    embedding length    4096
    quantization        Q4_K_M

  Capabilities
    completion
    tools
    thinking
python3 -m venv .venv
source .venv/bin/activate
pip install python-dotenv requests

Without a key, the local server creates a Free account with 5,000 credits a month on its first tool call. To use your own account, create one and save the key in .env.

ZENROWS_API_KEY=your_key_here

ollmcp is an open-source Python bridge that connects MCP servers to a local Ollama model over STDIO, SSE, and Streamable HTTP. It also installs the ollama and mcp packages the agent imports later. This tutorial was tested with ollmcp 0.35.0 on Python 3.14.

pip install ollmcp

Create zenrows-mcp.json.

{
  "mcpServers": {
    "zenrows": {
      "command": "sh",
      "args": ["-c", "set -a && . ./.env && set +a && exec npx -y @zenrows/mcp"]
    }
  }
}

The sh -c command sources .env into the environment before starting npx, so the JSON holds no secret and can be committed. Add .env to .gitignore, and run ollmcp from this directory because the .env path is relative.

ollmcp --servers-json zenrows-mcp.json --model qwen3:latest

On October 21, 2026, the bridge connected and listed the Zenrows tools (output trimmed).

Found server in config: zenrows
Connecting to server: zenrows
Successfully connected to zenrows with 62 tool(s) and 3 prompt(s) and 1 resource(s)/template(s)
╭──────────────────────────── 🔧 Available Tools ────────────────────────────╮
│ ✓ zenrows.scrape                         ✓ zenrows.extract                 │
│ ✓ zenrows.batch_create                   ✓ zenrows.batch_status            │
...
│ ✓ zenrows.browser_switch_tab             ✓ zenrows.browser_batch           │
╰─────────────────────────── 62/62 tools enabled ────────────────────────────╯
╭───────────────────────────────────────────────╮
│ Current model: qwen3:latest (ollama)          │
│ Capabilities: 🔧 Tools │ 💭 Thinking (medium) │
╰───────────────────────────────────────────────╯

Every enabled tool's schema goes into the model's context, so type /tools and keep scrape and extract switched on for a small local model. /human-in-the-loop asks you to approve each tool call before it runs.

The script starts the Zenrows MCP server with npx, so it doesn't need ollmcp or zenrows-mcp.json to run. Create agent.py. The pinned versions are in the repo's requirements.txt.

from dotenv import load_dotenv
load_dotenv()

import asyncio
import json
import os
import ollama
from mcp import ClientSession, StdioServerParameters
from mcp.client.stdio import stdio_client

SERVER = StdioServerParameters(
    command="npx",
    args=["-y", "@zenrows/mcp"],
    env=dict(os.environ),
)
MODEL = "qwen3:latest"
OPTIONS = {"num_ctx": 16384}
QUESTION = "Extract the page heading from https://www.scrapingcourse.com/antibot-challenge."
ALLOWED_ARGS = {"url"}

async def main():
    async with stdio_client(SERVER) as (read, write):
        async with ClientSession(read, write) as session:
            await session.initialize()

            listed = await session.list_tools()
            tools = [
                {"type": "function", "function": {
                    "name": t.name,
                    "description": t.description,
                    "parameters": t.input_schema,
                }}
                for t in listed.tools if t.name == "extract"
            ]

            messages = [{"role": "user", "content": QUESTION}]
            reply = ollama.chat(model=MODEL, messages=messages, tools=tools, options=OPTIONS)
            messages.append(reply["message"])

            for call in reply["message"].get("tool_calls") or []:
                args = {k: v for k, v in call.function.arguments.items() if k in ALLOWED_ARGS}
                print("calling", call.function.name, args)
                result = await session.call_tool(call.function.name, args)
                text = result.content[0].text
                print("result:", text[:300])
                data = json.loads(text).get("data")
                messages.append({"role": "tool", "content": json.dumps(data)})

            print(ollama.chat(model=MODEL, messages=messages, tools=tools, options=OPTIONS)["message"]["content"])

asyncio.run(main())

Three details matter on a local model.

OPTIONS sets the context window. Ollama uses num_ctx, Ollama truncated that prompt and the model answered from part of the listings. ALLOWED_ARGS filters the model's arguments and blocks anything the model invents, so only the URL reaches the server. Zenrows then turns on JavaScript rendering and premium proxies only when the page needs them. data object next to a few status flags. The script hands the model only that The test target is the Scrapingcourse antibot challenge page, which reports its own verdict.

python3 agent.py
Using existing API key from env (secrets dir: /Users/user/.zenrows)
Zenrows MCP server running on stdio (secrets dir: /Users/user/.zenrows)
calling extract {'url': 'https://www.scrapingcourse.com/antibot-challenge'}
result: {"ok":true,"mode":"auto","empty":false,"data":{"challenge_result":{"message":"You bypassed the Antibot challenge! :D","status":"bypassed"},"page":{"challenge_type":"antibot","title":"Antibot Challenge"}}}
The page heading (h1) from the URL is:

**Antibot Challenge**

This is the title extracted from the webpage, which also happens to be the name of the antibot challenge page.

For comparison, without_zenrows.py sends a plain request to the same page.

import requests

r = requests.get("https://www.scrapingcourse.com/antibot-challenge")
print(r.status_code, len(r.text))
python3 without_zenrows.py
403 5517

The request was blocked with a 403.

Confirm what sits in front of Indeed.

curl -sI https://www.indeed.com | grep -iE "^(server|date):"
date: Wed, 21 Oct 2026 08:15:02 GMT
server: cloudflare

Create cloudflare_site.py with the same code as agent.py and change the QUESTION line.

QUESTION = "List the company names and job titles from https://www.indeed.com/jobs?q=data+analyst&l=Austin%2C+TX."
python3 cloudflare_site.py
Using existing API key from env (secrets dir: /Users/user/.zenrows)
Zenrows MCP server running on stdio (secrets dir: /Users/user/.zenrows)
calling extract {'url': 'https://www.indeed.com/jobs?q=data+analyst&l=Austin%2C+TX'}
result: {"ok":true,"mode":"auto","empty":false,"data":{"job_listings":[{"benefits":null,"company_logo_url":null,"company_name":"Paywall Bypass","detail_url":"/rc/clk?jk=1d9d5ad6081ee5e0&bb=0Jmy1L_Rym9Qr_CYZLvqO-iSA0EMSX-Z6TVgVMeYzV9VdW2jJoxc32S2GChQtdu5Qm9Zo3b5B6zKmWui0wMrKaTa7wukcUUmE83DPPtvU-K-2B_k_liBF0N
Here are the company names and job titles from the Indeed search results in Austin, TX:

1. **Paywall Bypass** - Data Analyst  
2. **Visa U.S.A. Inc.** - Data & Insights Analyst  
3. **Texas Department of Transportation** - Data Analyst IV  
4. **Austin Community College** - Analyst, Institutional Research (Hourly)  
5. **Austin Community College** - Analyst, Human Resources Data  
6. **University of Texas at Austin** - Data Analyst, College of Liberal Arts  
7. **Steck Systems** - Data Analyst  
8. **Apple** - Business Insights Analyst, Apple Store Online Operations  
9. **Google** - Business Data Analyst II, Google Global Infrastructure, Strategy and Operations  
10. **Google** - Business Data Analyst II, Google Global Infrastructure, Strategy and Operations  
11. **University of Texas at Austin** - Senior Data Analyst  
12. **The University of St. Augustine for Health...** - Institutional Research Analyst  
13. **Ericsson** - Data Analyst Strategy and Operations  
14. **Texas Department of Transportation** - Business Analyst II  
15. **SHI International Corporation** - Associate Analytics Engineer - Data & Visualization  
16. **Health & Human Services Comm** - Data Analyst V  

Let me know if you'd like additional details like locations or salary information!

The extraction returned ok: true with 16 job listings and no challenge response, and the model listed every company and job title on the page.

Here is how three MCP servers compared, as documented on October 21, 2026.

One MCP configuration gave a local Ollama agent access to live pages, including Cloudflare-protected ones, while Zenrows handled access, rendering, and proxies for every request. For a task spanning several pages, ask in plain English and the agent can use the crawl and batch tools.

agents.zenrows.com.