Pumperly FAQs

Question 1

What is Pumperly-MCP and its core purpose?

Accepted Answer

Pumperly-MCP acts as a tiny bridge, exposing Pumperly instances as an MCP server. Its core purpose is to enable Large Language Models (LLMs) and AI agents to access real-time fuel and EV charging prices, perform station searches, plan routes, and geocode locations.

Question 2

How does Pumperly-MCP facilitate interaction with LLMs?

Accepted Answer

Pumperly-MCP utilizes the Model Context Protocol (MCP), allowing LLMs to initialize a session, read available resources (like configuration or statistics), list tools, and then call specific tools like 'find_nearest_stations' or 'calculate_route' through a JSON-RPC API.

Question 3

What specific data and tools can LLMs access through Pumperly-MCP?

Accepted Answer

LLMs can query real-time fuel and EV charging prices, find nearest stations or stations within a specified area, calculate and plan routes, discover stations along a route, and perform geocoding of locations. All functionalities are exposed via a single JSON-RPC endpoint.

Question 4

What are the common deployment methods for Pumperly-MCP?

Accepted Answer

Pumperly-MCP can be easily deployed using Docker Compose for containerized environments, installed globally via npm for stdio transport compatibility, or built locally from source. It primarily requires configuring the Pumperly instance URL.

Question 5

Does Pumperly-MCP support both traditional fuel and EV charging needs?

Accepted Answer

Yes, Pumperly-MCP is designed to provide real-time data for both traditional fuel stations (petrol/gas) and electric vehicle (EV) charging points, making it a comprehensive tool for various mobility-related applications.

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