steam-mcp
Allows interaction with the Steam Web API and storefront, enabling library analysis, recently played games, wishlist retrieval, app details, reviews, game search, backlog recommendations based on mood/tags, and discovery of well-reviewed games the user does not own.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@steam-mcp recommend me a cozy game to play from my library"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
steam-mcp
An MCP (Model Context Protocol) server that connects AI assistants to the Steam Web API and storefront services. Enables game library analysis, backlog recommendations based on mood, wishlist evaluation, and storefront searches. Works alongside itad-mcp and opencritic-mcp.
What this does
This server acts as the foundational layer of a 3-server gaming stack:
steam-mcp(this one) — understands your Steam library, playtime, backlog, wishlist, and tastes. It surfaces game AppIDs.opencritic-mcp— takes game names/AppIDs and provides detailed critic scores and reviews.itad-mcp— takes game names/AppIDs and finds the best deals across legitimate storefronts and historical lows.
Example workflow: User: "I want something atmospheric and chill to play tonight."
steam-mcp(recommend_from_library) finds unplayed "Atmospheric" + "Relaxing" games in your backlog.opencritic-mcpvalidates which of those games have great critic scores.User: "These look great, any of them on sale?"
itad-mcptakes the AppIDs and checks if any are on sale right now.
Wishlist deal check: get_wishlist → take AppIDs → itad-mcp get_historical_low → surface any games at or near their historical low price.
Related MCP server: steam-mcp
Tools
Tool | Parameters | Description | Requires API Key? |
|
| Retrieve the user's owned Steam game library with playtime statistics. | Yes |
|
| Games played in the last 2 weeks. | Yes |
|
| Retrieve the user's Steam wishlist to find interesting games. | Yes |
|
| Full Steam Store metadata for a specific game (description, genres, categories, price, metacritic). | No |
|
| Steam user review sentiment for a game. | No |
|
| Resolve a game title to a Steam AppID. | No |
|
| Find games in the user's library that match specified tags/mood. | Yes |
|
| Find well-reviewed games on Steam matching specific tags that the user does NOT own. | Optional |
Prerequisites
Node 18+
Steam account with a public library
Steam Web API key
Getting a Steam API Key
Log in with your Steam account.
Enter any domain name (e.g.
localhost).Click "Register" and copy the generated 32-character API key.
Finding your Steam ID
You can use either your vanity name or your 17-digit SteamID64.
If your profile URL is
https://steamcommunity.com/id/your_vanity_name/, your Steam ID isyour_vanity_name.If your profile URL is
https://steamcommunity.com/profiles/76561198012345678/, your Steam ID is76561198012345678.
Make your profile public
For the API to read your library and playtime, your Game Details privacy setting must be Public.
Go to your Steam Profile -> Edit Profile -> Privacy Settings.
Set "My Profile" to Public.
Set "Game details" to Public.
Set "Inventory" to Public if you want wishlist access.
Installation
Option A: Using npx (Recommended)
Add this to your MCP configuration JSON (mcp_config.json):
{
"mcpServers": {
"steam": {
"command": "npx",
"args": ["-y", "steam-user-mcp"],
"env": {
"STEAM_API_KEY": "your_api_key_here",
"STEAM_ID": "your_vanity_name"
}
}
}
}Option B: Clone & Build
git clone https://github.com/brandikun/steam-mcp
cd steam-mcp
npm install
npm run buildThen add this to your MCP config:
{
"mcpServers": {
"steam": {
"command": "node",
"args": ["/path/to/steam-mcp/dist/index.js"],
"env": {
"STEAM_API_KEY": "your_api_key_here",
"STEAM_ID": "your_vanity_name"
}
}
}
}MCP Configuration variables
STEAM_API_KEY(Required): Your 32-character Steam Web API key.STEAM_ID(Optional): The default user to query for library and playtime tools. If not provided, it must be passed per tool call.
Usage with itad-mcp and opencritic-mcp
For the ultimate gaming assistant, combine all three:
{
"mcpServers": {
"steam": {
"command": "npx",
"args": ["-y", "steam-user-mcp"],
"env": { "STEAM_API_KEY": "your_api_key", "STEAM_ID": "your_id" }
},
"itad": {
"command": "npx",
"args": ["-y", "itad-mcp"],
"env": { "ITAD_API_KEY": "your_itad_key" }
},
"opencritic": {
"command": "npx",
"args": ["-y", "opencritic-mcp"],
"env": { "RAPIDAPI_KEY": "your_rapidapi_key" }
}
}
}Troubleshooting
"No Steam ID provided": You must set
STEAM_IDin your environment or provide it in the tool call."Steam API key is required": The
STEAM_API_KEYenvironment variable is missing."Vanity URL not found": The Steam ID provided couldn't be resolved. Ensure it's your exact vanity name or a valid SteamID64.
Library returns empty: Your profile is private. See the "Make your profile public" section above.
Tags not matching: Steam tags are very specific. The server does its best with fuzzy matching, but if a tag isn't found, try a broader synonym (e.g., "RPG" instead of "Role-playing").
Privacy
Credentials are only read from environment variables. No data is logged or sent anywhere except Steam's own APIs.
License
MIT
Available Tools
8 toolsdiscover_gamesA
Find well-reviewed games on Steam matching specific tags that the user does NOT own. Returns AppIDs — pass them to itad-mcp for pricing and deal data. Use when looking for new purchases.
Inputs: tags (required), steam_id, max_results, country_code.
get_app_detailsA
Full Steam Store metadata for a specific game: description, genres, categories, price, platforms, Metacritic score. Use when evaluating a specific candidate. Pair with get_app_reviews for Steam sentiment and opencritic-mcp for critic scores.
Inputs: appid (required), country_code.
get_app_reviewsB
Steam user review sentiment for a game: overall verdict, positive %, and total review count. Use alongside opencritic-mcp critic scores for a complete quality picture.
Inputs: appid (required), filter.
get_libraryA
Retrieve the user's owned Steam game library with playtime statistics. Use to understand what the user owns, identify backlog (unplayed), or check playtime for specific games. Sort by 'recent' to see current obsessions. Filter 'unplayed' for backlog candidates.
Inputs: sort, limit, filter, steam_id.
get_recently_playedA
Games played in the last 2 weeks — the strongest signal for the user's current mood and taste. Call this before recommend_from_library to understand recent preferences.
Inputs: steam_id.
get_wishlistA
Retrieve the user's Steam wishlist. Use this to find games the user has already flagged as interesting, then pass the AppIDs to itad-mcp's get_game_prices or get_historical_low to check for deals. This is the most targeted buy-signal workflow.
Inputs: sort, limit, steam_id.
recommend_from_libraryA
Find games in the user's library that match specified tags/mood. Ideal for backlog recommendations. The LLM should derive tags from the user's mood or preferences (e.g., 'relaxing' -> ['Casual', 'Puzzle', 'Atmospheric']). Returns ranked matches with tag overlap scores.
Inputs: tags (required), limit, steam_id, unplayed_only.
search_gamesA
Resolve a game title to a Steam AppID. Use when you know a game's name but need its AppID for other tools. Returns multiple matches ranked by relevance.
Inputs: query (required), limit.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
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TDQS
Scored across 8 tools
Each tool targets a distinct resource and action: recently played, library, wishlist, app details, reviews, title search, library recommendations, and new discoveries. No two tools overlap in purpose, making selection unambiguous.
All tools follow a clear verb_noun or verb_preposition_noun pattern (get_recently_played, get_library, search_games, recommend_from_library, discover_games). The naming style is consistent throughout.
8 tools is well-scoped for a Steam discovery server. Each tool addresses a core need (library, wishlist, metadata, reviews, search, recommendations) without redundancy or bloat.
The tool set covers the primary discovery and recommendation workflows: assessing user preferences, evaluating specific games, and finding new titles. Minor gaps exist (e.g., no direct wishlist management or similar-game lookup), but these are not critical dead ends.
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