Loopnet MCP Server

An MCP (Model Context Protocol) server that provides Claude Code with real-time access to commercial real estate data from Loopnet, the largest CRE marketplace in the US. Loopnet has no public API — this server scrapes the site and exposes structured data through three tools that any Claude Code session can call via natural language.

What It Does

Once registered with Claude Code, you can ask things like:

• “Find office buildings for sale in Houston, TX under $5M”
• “Get details on this Loopnet listing: https://www.loopnet.com/Listing/…”
• “Give me a market overview for retail properties in Miami, FL”

The server fetches live data from Loopnet, parses the HTML, and returns structured results — property names, addresses, prices, sizes, cap rates, broker info, images, and more.

Tools

Supported Property Types

office, retail, industrial, multifamily, land, hospitality, special-purpose, health-care

Supported Listing Types

for-sale, for-lease

Location Formats

• City and state: "Houston, TX", "New York, NY"
• State abbreviation: "TX"
• Zip code: "77001"

Setup

Prerequisites

• Python 3.10+
• Claude Code CLI installed

Installation

git clone  && cd LoopnetMCP

# Create virtual environment and install
python3 -m venv .venv
source .venv/bin/activate
pip install -e ".[dev]"

Register with Claude Code

claude mcp add \
  --scope user \
  --transport stdio \
  loopnet \
  -- python3 /path/to/LoopnetMCP/src/loopnet_mcp/server.py

Use --scope user to make the server available in all Claude Code sessions, or --scope project to restrict it to a single project directory.

After registration, restart Claude Code. Verify with /mcp — the loopnet server should appear as connected with 3 tools.

Configuration

All settings are optional and have sensible defaults. Override via environment variables or a .env file (see .env.example):

Architecture

src/loopnet_mcp/
├── server.py              # FastMCP server + 3 tool definitions (entry point)
├── models.py              # Pydantic v2 data models
├── config.py              # Configuration via pydantic-settings
├── cache.py               # In-memory TTL cache
├── __main__.py            # python -m loopnet_mcp support
└── scraper/
    ├── client.py          # HTTP client (curl_cffi) with rate limiting, retries, caching
    ├── browser.py         # nodriver-based browser fallback for JS challenges
    ├── urls.py            # URL construction and normalization
    └── parsers/
        ├── search.py      # Search results HTML → PropertySummary list
        ├── detail.py      # Property detail HTML → PropertyDetail
        ├── market.py      # Price/size parsing + market aggregation
        └── utils.py       # Shared address parsing

Request Flow

Claude Code natural language query
  → MCP tool call (server.py)
    → URL builder (urls.py)
      → HTTP client (client.py, curl_cffi)
        → [If JS challenge detected] → Browser fallback (browser.py, nodriver)
      → HTML Parser (parsers/)
        → Pydantic model (models.py)
          → JSON dict response back to Claude

Anti-Bot Bypass

Loopnet uses Akamai Bot Manager with two layers of bot detection:

1. TLS Fingerprinting (JA3/JA4) — Standard Python HTTP libraries (httpx, requests, aiohttp) are blocked instantly with HTTP 403 because their TLS Client Hello handshake has a non-browser signature. This project uses curl_cffi which wraps curl-impersonate to replicate Chrome 136’s exact TLS fingerprint.

2. JavaScript Challenge — Even with correct TLS fingerprints, Akamai sometimes serves a short JS challenge page (HTTP 200, ~2500 chars with sec-if-cpt-container marker) that must be executed by a real browser. When detected, the client falls back to nodriver (undetectable headless Chrome) to solve the challenge and extract the real page content.

Data Models

PropertySummary — Search result card: name, address, city, state, zip, property type, price, size, URL, image, broker info.

PropertyDetail — Full listing page: everything in summary plus cap rate, NOI, year built, building class, zoning, parking, stories, units, description, highlights, images, broker phone.

SearchResult — Container: query metadata + list of PropertySummary.

MarketOverview — Aggregated stats: total listings, average price, average price/SF, average size, price range, size range, listing type breakdown, property subtype breakdown, sample listings.

Caching

An in-memory TTL cache (default 5 minutes, 500 entries max) prevents redundant requests. Cache keys are full URLs. When at capacity, the oldest entry is evicted. The cache is not persisted across server restarts.

Rate Limiting

Requests are rate-limited with a configurable minimum delay (default 3 seconds) and concurrency limiter (default 1 concurrent request). Exponential backoff is applied on retries (2^attempt seconds).

Error Handling

MCP tools never raise exceptions to the framework. All errors are caught and returned as JSON dicts with an "error" key:

{"error": "Blocked by Loopnet (403) for URL: ...", "query_location": "Dallas, TX", "properties": []}

This allows the LLM to read the error and provide a human-friendly explanation to the user.

Development

Running Tests

# All tests
python3 -m pytest tests/ -v

# Specific test file
python3 -m pytest tests/test_parsers.py -v

# Specific test
python3 -m pytest tests/test_market.py::TestParsePrice::test_parse_price_dollars -v

All tests use mocked HTML fixtures from tests/fixtures/ — no real HTTP requests are made. The conftest.py auto-patches the warmup request and browser launcher.

Test Files

Updating Parsers for HTML Changes

Loopnet periodically changes their HTML structure, which will break the CSS selectors in parsers/search.py and parsers/detail.py. When this happens:

1. Save the new HTML from a real browser to tests/fixtures/
2. Update the CSS selectors in the relevant parser
3. Update the corresponding test assertions
4. Run the full test suite to check for regressions

Diagnostic Commands

# Check if Loopnet returns real content or a challenge page
python3 -c "
import asyncio
from loopnet_mcp.scraper.client import LoopnetClient
async def test():
    client = LoopnetClient()
    try:
        html = await client.fetch('https://www.loopnet.com')
        if 'sec-if-cpt-container' in html:
            print('CHALLENGE PAGE (Akamai JS challenge)')
        elif len(html) > 10000:
            print('REAL CONTENT (' + str(len(html)) + ' chars)')
        else:
            print('UNKNOWN (' + str(len(html)) + ' chars)')
    except Exception as e:
        print(f'ERROR: {e}')
    finally:
        await client.close()
asyncio.run(test())
"

# Verify MCP server registration
claude mcp get loopnet

# Verify server starts cleanly
python3 -c "from loopnet_mcp.server import mcp; print('OK:', mcp.name)"

Tech Stack

Known Limitations

• First-page-only aggregation: get_market_overview fetches only the first page of search results. Multi-page crawling would be slow due to rate limiting.
• Akamai JS challenges: If the headless browser fallback fails to solve the challenge, the tool returns an error dict. This can happen if Akamai escalates to CAPTCHA-level challenges.
• Price parsing: Prices are kept as strings in search/detail results. Only get_market_overview converts them to numbers for aggregation. Unusual formats (e.g., "$25/SF/YR") are skipped.
• No real-time updates: Cache TTL is 5 minutes. Listings may appear stale within that window.

License