3dcitydb-mcp-server

v0.2.2 safe
1.9
Low Risk

MCP server for natural-language querying of 3DCityDB v5 semantic city models

🔬 Heuristic Checks

⚠ Outbound Network Calls score 9.0

Found 6 network call pattern(s)

  • p("/") try: req = urllib.request.Request(f"{base}/api/tags", headers={"Accept": "application/
  • lication/json"}) with urllib.request.urlopen(req, timeout=5) as resp: data = json.loa
  • n False try: with urllib.request.urlopen( urllib.request.Request(f"{base}/api/tag
  • .request.urlopen( urllib.request.Request(f"{base}/api/tags"), timeout=3 ) as resp:
  • p("/") try: req = urllib.request.Request( f"{base}/api/show", data=js
  • POST", ) with urllib.request.urlopen(req, timeout=5) as resp: data = json.loa
✓ Code Obfuscation

No obfuscation patterns detected

✓ Shell / Subprocess Execution

No shell execution patterns detected

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: tum.de>

⚠ Suspicious Page Links score 6.0

Found 3 suspicious link(s) on the package page

  • Non-HTTPS external link: http://your-server:8080/sse`
  • Non-HTTPS external link: http://your-server:8080/health`
  • Non-HTTPS external link: http://host.docker.internal:11434
✓ Git Repository History

Repository tum-gis/3dcitydb-mcp-server appears legitimate

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • Author name is missing or very short
  • Author "" appears to have only 1 package on PyPI (new or inactive account)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with 3dcitydb-mcp-server 
Develop a mini-application named 'CityQueryExplorer' using Python and the '3dcitydb-mcp-server' package. This application will allow users to interactively query and explore semantic data from 3DCityDB v5 city models using natural language queries. The app should have a user-friendly interface where users can input their queries and receive visual representations of the queried data, such as 3D models or maps.

Step-by-Step Requirements:
1. Setup: Install necessary Python packages including '3dcitydb-mcp-server', 'flask' for web development, and 'matplotlib' for visualization.
2. User Interface: Create a simple HTML/CSS frontend using Flask, allowing users to enter their natural language queries and view results.
3. Backend Integration: Use '3dcitydb-mcp-server' to process the user's query and retrieve relevant information from the 3DCityDB v5 semantic city model.
4. Data Visualization: Implement a feature to visualize the retrieved data either through static images or interactive 3D models using available Python libraries.
5. Error Handling: Ensure the application provides meaningful feedback in case of errors or invalid queries.
6. Documentation: Provide comprehensive documentation detailing how to set up and use the application.

Suggested Features:
- Support for multiple query types (e.g., 'Find all buildings taller than 100 meters', 'Show me parks near the river').
- Integration with a map service like OpenStreetMap to provide geographical context.
- Option to save and share queries and results.
- Real-time updates for live city model changes.

The '3dcitydb-mcp-server' package is central to this application as it enables natural language processing capabilities and seamless interaction with the 3DCityDB v5 database, making complex city model data accessible and understandable to non-technical users.