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MCP EV Assistant Server

@Abiorh001

Repository

About MCP EV Assistant Server

A powerful server implementation for managing Electric Vehicle (EV) charging stations, trip planning, and resource management. This server provides a comprehensive set of tools and APIs for EV-related services.

Config

Add this server to your MCP-compatible client using the configuration below.

{
  "mcpServers": {
    "mcp_ev_assistant_server": {
      "command": "python",
      "args": [
        "-m",
        "venv",
        ".venv"
      ]
    }
  }
}

Tools

4

Find EV charging stations near a location

`address`: Location to search around (string, required)

Plan an EV-friendly route

`user_address`: Starting location (string, required)

Overview

What is MCP EV Assistant Server?

MCP EV Assistant Server is a Python-based MCP server for managing Electric Vehicle (EV) charging stations, trip planning, and resource management. It provides tools and APIs for locating charging stations, planning EV-friendly routes, and handling EV-related PDF documents. It is intended for developers building EV assistant applications.

How to use MCP EV Assistant Server?

Clone the repository, set up a Python virtual environment, install dependencies with uv sync, and configure environment variables OPENCHARGE_MAP_API_KEY and GOOGLE_MAP_API_KEY in a .env file or servers_config.json. Start the server with python ev_assistant_server.py. Invoke tools like charge_points_locator and ev_trip_planner via an MCP client.

Key features of MCP EV Assistant Server

  • Locate EV charging stations by address, distance, and socket type
  • Plan EV-friendly routes with automatic charging stop integration
  • Consider vehicle range and current charge level
  • Manage PDF documents with automatic text extraction
  • Support resource subscriptions for real-time updates
  • Provide interactive prompts for charging and route planning

Use cases of MCP EV Assistant Server

  • Find nearby CCS or Type 2 charging stations in a city
  • Plan a road trip between two locations with optimal charging stops
  • Estimate charging time for a given vehicle model and charger power
  • Browse and extract text from EV user manuals stored as PDFs

FAQ from MCP EV Assistant Server

What API keys are needed?

You need an OpenCharge Map API key and a Google Maps API key. Set them in the .env file or servers_config.json.

How do I configure the server?

Create a .env file with the two API keys, or add them to the env section of servers_config.json. The server expects a Data/ directory for PDF resources.

Can I add custom tools or prompts?

Yes. Define the tool schema in core.schemas, implement the tool in agentTools, and register it in the tool handlers. Prompts are added in the PROMPTS dictionary with validation in handle_get_prompt().

Where are PDF resources stored?

PDF files are placed in the Data/ directory. The server automatically discovers them and supports text extraction with fallback methods.

What transport does the server use?

The server is an MCP server; the README shows tool calls via client.call_tool, indicating it follows the MCP protocol. Transport details are not explicitly stated, but the implementation implies standard MCP transport (e.g., stdio or HTTP).

Frequently asked questions

What API keys are needed?

You need an OpenCharge Map API key and a Google Maps API key. Set them in the `.env` file or `servers_config.json`.

How do I configure the server?

Create a `.env` file with the two API keys, or add them to the `env` section of `servers_config.json`. The server expects a `Data/` directory for PDF resources.

Can I add custom tools or prompts?

Yes. Define the tool schema in `core.schemas`, implement the tool in `agentTools`, and register it in the tool handlers. Prompts are added in the `PROMPTS` dictionary with validation in `handle_get_prompt()`.

Where are PDF resources stored?

PDF files are placed in the `Data/` directory. The server automatically discovers them and supports text extraction with fallback methods.

What transport does the server use?

The server is an MCP server; the README shows tool calls via `client.call_tool`, indicating it follows the MCP protocol. Transport details are not explicitly stated, but the implementation implies standard MCP transport (e.g., stdio or HTTP).

Comments

Basic information

Category

AI & Agents

Transports

stdio

Publisher

Abiorh001

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