rivalsdata-api

An unofficial Python client for RivalsData's public Marvel Rivals data. It uses the site's undocumented API, so routes and fields can change. The client keeps unknown response fields accessible instead of discarding them.

Install

Python 3.10 or newer:

python -m pip install rivalsdata-api

For editable development, clone the repository and run python -m pip install -e '.[dev]'. The optional Camoufox Cloudflare fallback is installed with python -m pip install 'rivalsdata-api[browser]', followed by python -m camoufox fetch.

Quick start

from rivalsdata import RivalsDataClient, hero_id, hero_name

print(hero_name(1016))  # Loki
print(hero_id("Loki"))  # 1016

with RivalsDataClient() as rd:
    player = rd.get_player("GS-")  # numeric UID works too
    print(player.name, player.level, player.rank_game_season)

    # Player profile sections are lazy resource managers.
    hero_season = player.heroes.fetch(season=20)
    all_hero_seasons = player.heroes.fetch(season="all")
    map_stats = player.stats.maps(season=20)
    match_page = player.matches.fetch(season=20)

    # Current match (None if the profile is not currently in a game).
    live_game = player.live_game.fetch()
    if live_game is not None:
        print(live_game.players, live_game.team_avg_rank)

    # Site-wide resources are available from the client.
    leaderboard = rd.leaderboards.fetch(limit=100, season=20, platform=1)
    tier_list = rd.heroes.tier_list(platform=1, rank="grandmaster_plus")
    team_ups = rd.team_ups.fetch(platform=1, rank="grandmaster_plus")
    xp_page = rd.insights.xp()

print(hero_season[0].win_rate)  # integer percent when wins/losses are present

MCP server (ChatGPT and Claude)

Install the MCP extra and the package:

python -m pip install 'rivalsdata-api[mcp]'

The server exposes read-only tools for player search and profiles, a player's current live match (when they are in one), match history, player stats, leaderboards, heroes, team-ups, public insights, matches, and factions. The show_player_dashboard tool returns an MCP-UI player card with rank and competitive record plus one optional data section per call: current match roster, hero win-rate chart, or recent match form with K/D/A. This keeps each dashboard pull to the profile plus at most one additional endpoint. It supports local stdio for Claude Desktop and Streamable HTTP for remote MCP clients such as ChatGPT. Data comes from RivalsData's undocumented API and may change; profile match history can be private.

Known hero_id and top_hero_id fields in MCP results include corresponding hero_name and top_hero_name fields. The resolve_hero tool accepts either a hero name or numeric ID. The pip package also exports hero_name(id) and hero_id(name); returned DataModel rows provide .hero_name and .top_hero_name conveniences. Those resolved fields are included in mapping iteration and .to_dict() output to simplify serialization; .raw remains the untouched source payload.

How the MCP UI works

show_player_dashboard fetches current data, then returns an HTML UI resource alongside the tool result. It advertises the dashboard through _meta.ui.resourceUri, uses the text/html;profile=mcp-app resource MIME type, and registers that URI for resources/read so the host can actually load the app frame. The tool result carries the rendered dashboard as structured content for the app frame and an embedded HTML resource for older MCP-UI clients. It also includes openai/outputTemplate as a ChatGPT compatibility alias. Hosts without UI support still receive a text result and can use the regular MCP tools. The dashboard is a snapshot from the time the tool runs; ask for it again to refresh. The section argument defaults to live_match; use hero_form or recent_matches in separate calls when you need those views.

Claude Desktop (local)

Add a server entry to Claude Desktop's claude_desktop_config.json, replacing the path with the Python executable in the environment where the extra is installed:

{
  "mcpServers": {
    "rivalsdata": {
      "command": "C:\\path\\to\\venv\\Scripts\\python.exe",
      "args": ["-m", "rivalsdata.mcp_server"]
    }
  }
}

On macOS/Linux, use the environment's bin/python path. Restart Claude Desktop after saving the configuration.

ChatGPT or remote Claude connector

Run the server on a host reachable over HTTPS:

uvicorn rivalsdata.mcp_server:app --host 0.0.0.0 --port 8000

The MCP endpoint is /mcp (for example, https://your-host.example/mcp). Add that endpoint through the client's custom/remote MCP connector settings. The server does not implement authentication; put it behind an authenticated HTTPS gateway before exposing it publicly. For local development, bind to 127.0.0.1 instead. The app is the MCP SDK's Streamable HTTP ASGI application; Uvicorn manages its lifespan and session manager.

Every implemented response route now has named endpoint models and row models with annotations for fields observed in the API inventory. This includes Player, Match, MatchHistory, MatchTeam, MatchPlayer, Character, ProficiencyResponse, LeaderboardResponse, PunishmentsPage, XPPage, Top500Response, and typed teammate, crosshair, stats, faction, and insight records. For example, rd.matches.get(match_id) returns a Match, player.matches.fetch() returns a MatchHistory, and player.proficiency.fetch() returns a ProficiencyResponse. Nested match teams and participants are converted to MatchTeam and MatchPlayer; embedded character records use Character. Models support mapping access (player["level"]) and attribute access (player.level). Unknown upstream fields are still preserved and available through .raw; endpoint schemas that have not been observed completely are annotated only for known fields.

with RivalsDataClient() as rd:
    player = rd.get_player(1970288503)             # Player
    proficiency = player.proficiency.fetch()       # ProficiencyResponse
    account = next(iter(proficiency.accounts.values()))  # Proficiency
    hero = account.hero_proficiency_infos["1011"]   # HeroProficiency
    print(hero.proficiency_level, hero.proficiency_point)

    tier_list = rd.heroes.tier_list()               # TierListResponse
    print(tier_list.heroes[0].hero_id)             # Character

Public resources

• rd.leaderboards.fetch(...) — global player ranking.
• rd.heroes.tier_list(...), .get(hero_id), .meta(hero_id, range=90), .leaderboard(hero_id, **filters) — hero metrics and ranking.
• rd.team_ups.fetch(...) — team-up stats.
• rd.insights.punishments(...), .xp(...), .top_500(...), .commbans(...), .leavers(...) — public insights and cursor metadata.
• rd.factions.get(faction_id), rd.matches.get(match_id), rd.profiles.get(username), and rd.favorites.fetch(uids) — detail/profile lookups.
• player.heroes.fetch(...), .matches.fetch(...), .live_game.fetch(), .teammates.fetch(...), .crosshairs.fetch(), .proficiency.fetch(), .punishments.fetch(), .name_history.fetch() — profile sections.
• player.stats.heroes(...), .maps(...), .bans(...) — detailed profile stats.

See the observed API inventory for methods, parameters, observed response shapes, and endpoints that require a RivalsData account. The API inventory distinguishes observed behavior from inferred/unverified details.

Cloudflare fallback

Requests use curl_cffi with a Chrome TLS profile by default. If blocked, enable the optional browser fallback:

with RivalsDataClient(use_browser_fallback=True) as rd:
    player = rd.get_player(1970288503)

Errors and contributions

All package exceptions inherit from RivalsDataError. See CONTRIBUTING.md for setup, code layout, change workflow, and notes for new contributors. docs/PROJECT_CONTEXT.md is the handoff document for new coding sessions.

This project is not affiliated with RivalsData, NetEase, or Marvel. Keep request rates reasonable and respect the site's terms.