Videogame Encyclopedia MCP Server

Created By
Hoani CROSS5 months ago
A Model Context Protocol (MCP) server that provides structured video game information from Steam and SteamGridDB. This server exposes tools for searching games and retrieving comprehensive metadata including descriptions, categories, release dates, player counts, and visual assets like logos, boxart, and icons.
Overview

What is Videogame Encyclopedia MCP Server?

The Videogame Encyclopedia MCP Server is a Model Context Protocol (MCP) server that provides structured information about video games sourced from Steam and SteamGridDB. It allows users to search for games and retrieve detailed metadata, including descriptions, categories, release dates, player counts, and visual assets such as logos and boxart.

How to use Videogame Encyclopedia MCP Server?

To use the server, clone the repository, install the necessary dependencies, configure your API keys, and run the server. You can integrate it with clients like Claude Desktop or use it directly via command line.

Key features of Videogame Encyclopedia MCP Server?

  • Steam Integration: Search for games and retrieve detailed information including pricing, developer details, and supported platforms.
  • SteamGridDB Integration: Access visual assets for games, including logos and icons.
  • Unified Tools: Get a comprehensive game profile that aggregates data from both Steam and SteamGridDB.

Use cases of Videogame Encyclopedia MCP Server?

  1. Retrieving detailed game information for a gaming application.
  2. Displaying visual assets for games in a gaming website.
  3. Providing metadata for game reviews and articles.

FAQ from Videogame Encyclopedia MCP Server?

  • What API keys are required?

You need a SteamGridDB API key to access high-quality game assets.

  • Can I run this server locally?

Yes, you can run the server locally using Node.js.

  • What happens if a game is not found?

Ensure that the game ID is correct and sourced from SteamGridDB.

Server Config

{
  "mcpServers": {
    "game-encyclopedia": {
      "command": "npx",
      "args": [
        "videogame-encyclopedia-mcp-server"
      ],
      "env": {
        "STEAMGRIDDB_API_KEY": "your_steamgriddb_api_key_here"
      }
    }
  }
}
Project Info
Created At
5 months ago
Updated At
5 months ago
Author Name
Hoani CROSS
Star
-
Language
-
License
-

Recommend Servers

View All
Tavily Mcp
@tavily-ai

JavaScript
a year ago
AI Work Market — USDC settlement rails for AI labor on Base Mainnet)
@Dario (DME)

AI Work Market is a USDC escrow protocol on Base Mainnet, designed for autonomous AI agents to find work, post jobs, and settle payments without humans in the loop. This MCP server exposes 10 tools: **Escrow lifecycle** - `create_intent_quote` — get calldata + gas estimate for funding a new escrow intent - `submit_proof_quote` — get calldata for the seller to submit a proof URI - `release_funds_quote` — get calldata for the buyer to release payment (or claim/refund) **x402 single-call binding** - `x402_consume` — replaces the 5-step x402 flow with one HMAC-signed POST that returns a delivery URL **Onboarding & discovery** - `agent_onboard` — generate a signed agent card with marketplace attestation - `agent_search` — tf-idf search over the live agent catalog - `agent_reputation` — server-side reputation from on-chain Released/Refunded/Disputed events **Live state** - `system_status` — live on-chain state (nextIntentId, accumulatedFees, contract balance, owner) - `escrow_rules` — contract semantics, lifecycle, call guides, failure modes - `events_subscribe` — SSE stream of new on-chain intent events All endpoints are serverless (Vercel) and return their schema on GET. No browser, no wallet UI required for an agent to integrate. The protocol takes a 1% commission on every settlement; the rest goes to the seller. The full AgentCard is at `/.well-known/agent-card.json` (A2A-compatible). The OpenAPI 3.0.3 spec is at `/.well-known/openapi.json` with `components.securitySchemes` (none, hmacX402). `robots.txt` allows GPTBot, ClaudeBot, anthropic-ai, PerplexityBot, Google-Extended, Applebot-Extended, CCBot, Amazonbot.

36 minutes ago