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Czech Transport Assistant by CzechFabric | Český dopravní asistent od CzechFabric

Czech Transport Assistant by CzechFabric | Český dopravní asistent od CzechFabric

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github.com/czechfabricAI & Agents0Updated 65w ago
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About Czech Transport Assistant by CzechFabric | Český dopravní asistent od CzechFabric

The official Python SDK for CzechFabric, which provides access to Czech public-service capabilities including transport and geolocation. It supports trip planning and real-time stop information, with caching, retries, and structured errors.

Key features

  1. 01Plan public transport trips
  2. 02Retrieve real-time departures
  3. 03Geocode and reverse-geocode locations
  4. 04Find nearby stops and stop details
  5. 05List server tools and prompt summaries
  6. 06Built-in caching, retries, and errors

Use cases

  1. 01Plan a public transport journey
  2. 02Check departures from a stop or coordinates
  3. 03Find nearby stops and inspect zones
  4. 04Review available tools for an LLM assistant

MCP support

The SDK connects to the CzechFabric MCP server and lets clients list its tools and generate prompt summaries. Requests require an API key.

Configuration

Remote servers need no local install. Pick your client, then copy the config or install in one click.

Endpointhttps://mcp.czechfabric.cz
Copy install command
Claude CodeTerminal
Cursor.cursor/mcp.json
VS Code.vscode/mcp.json
Claude Desktopclaude_desktop_config.json
Windsurf~/.codeium/windsurf/mcp_config.json

Run commands in a terminal; paste JSON into the client’s MCP config and restart. Servers that need sign-in will prompt on first connect.

All endpoints
Streamable HTTPhttps://mcp.czechfabric.czAuth required

Tools

13
ParameterTypeRequiredDescription
formatstringNoResponse format: 'tone' (token-efficient TONE format, default), 'json' (standard JSON), or 'both' (includes both formats)
locationstringYesLocation name, address, or landmark in Prague (e.g., 'Karlovo náměstí', 'Wenceslas Square', 'Anděl')
radius_metersanyNoSearch radius in meters around the location (default: 2000m = 2km)

Details

From the project README

PyPI License Downloads

🇨🇿 CzechFabric SDK

The official Python SDK for accessing the Czech Intelligence Fabric (MCP) - a unified API for Czech public services including transport, geolocation, and contextual AI tooling.


🚀 Features

  • ✅ Plan public transport trips using OpenTripPlanner
  • 🚏 Real-time departures from stop names or coordinates
  • 🗺️ Geocode / Reverse-geocode locations
  • 📍 Find nearby stops
  • 📦 Fetch stop metadata and zone info
  • 🧠 List available tools and generate LLM-friendly summaries
  • 🔁 Built-in retry, caching, and error handling

📦 Installation

pip install czechfabric-sdk

⚡ Quick Start

from czechfabric_sdk.client import CzechFabricClient

client = CzechFabricClient(
    api_key="your-api-key",
    base_url="https://mcp.czechfabric.cz"
)

# Example: Plan a trip
trip = await client.plan_trip("Florenc", "Karlovo náměstí", departure_time="in 15 minutes")
print(trip)

📚 Usage Examples

Explore detailed examples in examples.md:


🧰 Tool Management

You can dynamically list tools exposed by the MCP server:

tools = await client.get_tool_names()
prompt = await client.get_tool_prompt_summary()
print(prompt)

This is helpful for LLM-based assistants or debugging available capabilities.


🔐 Authentication

All requests require an API key:

client = CzechFabricClient(api_key="your-api-key", base_url="https://mcp.czechfabric.cz")

You can generate your key from your CzechFabric user dashboard.


⚠️ Error Handling

The SDK provides structured exceptions:

  • NetworkError – server unreachable or timed out
  • InvalidAPIKeyError – auth failure
  • RateLimitExceededError – too many requests
  • ToolExecutionError – tool execution failed

🧪 Running Tests

pip install -r requirements-dev.txt
pytest tests/

📎 License

MIT © 2025 Aliyu Abdulbasit Ayinde


🌐 Project Links

Comments

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New to MCP?

From what the protocol is to installing your first server, these answers cover most first questions.

An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.