A local HTTP gateway for accessing Funda.nl listing details, price history, and search results optimized for AI agent workflows.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install funda-gateway
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install funda-gateway using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Funda Gateway?
Funda Gateway is a specialized technical tool designed to bridge the gap between AI agents and the Dutch real estate market. By operating as a local HTTP server, it provides programmatic access to Funda.nl, enabling agents to retrieve detailed property listings, historical price data, and perform complex searches. This skill is a core component for developers building property analysis tools within Openclaw Skills, ensuring that real estate data is accessible in a clean, structured JSON format.
Built on top of the pyfunda library, the gateway handles the complexities of web interaction, including parameter normalization and image processing. It is designed for local-only execution, prioritizing privacy and speed for developers who need to integrate real-time property data into their local automation pipelines. This makes it an essential asset for any developer using Openclaw Skills to monitor the housing market or build specialized real estate research assistants.
Funda Gateway Use Cases
- Automating property searches based on specific criteria like location, price, and energy labels.
- Performing historical price analysis to identify market trends or investment opportunities.
- Generating optimized image previews for AI agents to visually inspect listings.
- Building custom alert systems that trigger when new listings match specific search parameters in Openclaw Skills.
- Exporting structured property data for further analysis in spreadsheets or databases.
How Funda Gateway Works
- The user initializes a local Python environment and starts the gateway server on a designated port.
- AI agents or scripts send HTTP GET or POST requests to the local endpoint (e.g., search_listings or get_listing).
- The gateway processes the request, communicating with the Funda.nl backend to fetch the required data.
- For image requests, the gateway downloads, resizes, and compresses photos to create agent-friendly previews.
- The results are returned as a standardized JSON response, allowing the agent to continue its workflow with structured real estate data.
Funda Gateway Setup
To set up the gateway within Openclaw Skills, navigate to the skill root and initialize the virtual environment:
cd /path/to/skills/funda
if [ ! -d .venv ]; then
python3 -m venv .venv
fi
source .venv/bin/activate
pip install -r scripts/requirements.txt
Launch the server locally:
python scripts/funda_gateway.py --port 9090 --timeout 10
You can verify the server is running by performing a simple search via curl:
curl -sG "http://127.0.0.1:9090/search_listings" --data-urlencode "location=amsterdam" --data-urlencode "pages=0"
Funda Gateway Data Schema & Taxonomy
The skill organizes its data output into structured JSON envelopes. Below is the primary metadata taxonomy:
| Data Type | Key Attributes | Description |
|---|---|---|
| Listing | public_id, price, description | Full details returned from pyfunda. |
| Price History | date, price | Time-series data of listing price changes. |
| Search Results | count, items | List of properties matching search filters. |
| Image Preview | id, base64, url | Resized JPEG data or local file paths. |
When saving images, the skill defaults to a directory structure of previews/<listing-id>/<photo-id>.jpg within the skill root.
Funda Gateway Advanced Features
- Multi-page search result merging with configurable delays to handle large datasets efficiently.
- Dynamic image processing that supports custom resizing, quality settings, and base64 encoding for LLM vision tasks.
- Support for complex search filters including energy labels, object types, and radius-based geographic searches.
- Local TLS client shim to ensure robust scraping capabilities without requiring system-level binary dependencies in Openclaw Skills.
- Agent-friendly error handling with specific error codes for invalid parameters or upstream failures.
SKILL.md
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