rivals-api

An unofficial, multi-source Python tracker for public Marvel Rivals data. It combines RivalsData, RivalsTracker, and Tracker.gg, keeps source disagreements visible, and preserves unfamiliar response fields as providers change. The project also includes a read-only MCP server.

Install

Python 3.10 or newer:

python -m pip install rivals-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 'rivals-api[browser]', followed by python -m camoufox fetch.

Quick start

from rivals_api import RivalsClient, hero_id, hero_name

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

with RivalsClient() as rd:
    player = rd.get_player("GS-")  # numeric UID works too
    print(player.name, player.level, player.rank_game_season)
    print(player.win_rate)  # Current-season competitive win rate

    # 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(limit=20, season=20)
    all_matches = player.matches.fetch(limit="all")
    combined_rate = player.matches.fetch_win_rate(season=20)
    print(combined_rate.win_rate_pct, combined_rate.provider_rates)
    fast_rate = player.matches.fetch_win_rate()
    exact_rate = player.matches.fetch_win_rate(method="exact")
    cached_rate = player.matches.fetch_win_rate(method="cached")  # after fetch(limit="all")
    hero_rates = player.matches.fetch_hero_win_rates(method="exact")
    class_rates = player.matches.fetch_class_win_rates(method="cached")

    # Current match when its provider exposes it; Custom discovery is unsupported.
    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

RivalsDataClient remains an alias for RivalsClient. The old rivalsdata import path is also retained for existing projects. To migrate an existing installation, uninstall rivalsdata-api first, then install rivals-api; this avoids the two distributions sharing the compatibility-package files.

The player overview's overall win rate (player.win_rate) is the current-season competitive win rate. It uses the latest available competitive season with usable counts when a direct source rate is absent; it does not combine seasons. Hero and class stats use the season selector supplied to their own methods.

Calculated player class statistics are available through player.stats.classes(season=20) and the MCP get_player_stats tool with category="classes". Pass a numeric season ID for that season, or season="all" for combined all-seasons data, matching player.heroes.fetch. Omitting the season uses the endpoint default. player.stats.heroes also accepts season="all". Each row contains player_class (tank, support, dps), the official role, hero IDs, and separate competitive and quickplay totals for games, wins, losses, and available MVP/SVP counts.

with RivalsClient() as rd:
    player = rd.get_player("GS-")
    stats = player.stats.classes(season=20)
    all_seasons = player.stats.classes(season="all")
    for row in stats.classes:
        print(row.player_class, row.competitive.win_rate)
    print(stats.excluded)  # Unknown roles or incomplete win/loss records

For MCP, use get_player_stats(uid_or_name="GS-", category="classes", season=20) for one season, or season="all" for combined all-seasons stats. Both return the same class response structure.

Detailed hero stats require a mode and match the website's selected tab:

competitive = player.stats.heroes(mode="competitive", season="all")
quickplay = player.stats.heroes(mode="quickplay", season=20)
print(competitive[0].competitive.games)
print(competitive[0].rank)  # Hero leaderboard position, or None if unavailable

Only heroes with data for the chosen mode are returned, with that mode's nested stats and a mode label; the other mode is omitted. Rows are sorted by the selected mode's games played descending, with ties retaining the JSON order. The source returns both modes in one response; filtering and sorting happen in this package, as they do on the website. Existing calls to player.stats.heroes() must now supply mode. MCP also requires mode when get_player_stats uses category="heroes"; other categories do not require it. The separate summary method player.heroes.fetch() keeps its existing behavior.

Hero stats include the source's top-level rank, matching the #N displayed in the left-hand hero card. It is preserved for either mode and all-seasons requests when supplied by the source; it is not recalculated as a quickplay or all-seasons leaderboard position. Missing ranks are returned as None.

Win rates are total wins / (total wins + total losses), rounded to an integer percent. They are weighted by hero records, rather than averaging hero win rates. The response's metadata identifies the source, formula, and requested season scope. Upstream hero-switch attribution is unknown; hero records may overlap within a match, so these totals cannot establish distinct match counts or the player's overall match win rate. All-seasons coverage is limited to records returned by the source; complete lifetime coverage is unverified. Excluded rows also produce a metadata warning. Empty modes have a None win rate. Role mappings were observed on RivalsData on 2026-09-30, including Deadpool's separate role IDs; generic Deadpool and unknown IDs are excluded rather than assigned a guessed class.

Character playtime was checked with Camoufox on 2026-09-30. Player hero stats did not expose cumulative hours, including in All Seasons. Match details do provide seconds in match.teams[].players[].heroes[].play_time; the site shows these as minutes and seconds when hovering a hero portrait. Sum the relevant player's entries across distinct retrieved matches and divide by 3600 to get character hours for those matches. Incomplete history prevents treating this as a lifetime total. See the playtime investigation for the observed fields and example.

MCP server (ChatGPT and Claude)

Install the MCP extra and the package:

python -m pip install 'rivals-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 one selected data section. Provider enrichment can make additional requests to compare the reported values. It supports local stdio for Claude Desktop and Streamable HTTP for remote MCP clients such as ChatGPT. Data comes from public provider endpoints and may be incomplete, private, or stale. Live Custom-game discovery is not currently implemented.

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": {
    "rivals-api": {
      "command": "C:\\path\\to\\venv\\Scripts\\python.exe",
      "args": ["-m", "rivals_api.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 rivals_api.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(limit=...) returns a MatchHistory. The required limit is either a positive number (bounded fetch with a resumable cursor) or "all" (fetch every available page); both combine and deduplicate providers. The overall, hero, and class win-rate methods accept method="estimate" (the default), "exact", or "cached". Overall estimates average available RivalsData/RivalsTracker competitive rates; hero estimates average each provider's per-hero rates; class estimates aggregate provider hero-participation counts within each class before averaging provider rates. These estimates are fast and approximate, and include provider sample counts. Exact calculations traverse all available history pages and deduplicate by match ID. Hero/class exact calculations also inspect match details and assign each match to the player's longest-played hero, which can require one detail lookup per match. Cached calculations make no requests and reuse a prior matches.fetch(limit="all") in the same Python process, including across client instances. If the cached history fetch had provider errors, the calculation does not retry missing data; inspect its coverage/errors. Cached hero/class rates use cached playtime details when available and otherwise fall back to the history row's hero. The returned metadata reports sources, unknown results, provider errors, and hero-attribution fallback counts. See the win-rate method guide for the assumptions and costs of each method. MCP tools expose the same three methods. 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 RivalsClient() 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

For a player selection list, use rd.search_players("silo"), or the MCP tool search_players(name="silo"). Each candidate includes a name and numeric UID; pass the selected UID to get_player_profile(uid=283622404) for their profile overview. search_player_candidates remains an alias, and get_player still accepts a UID or exact name. MCP search_players now returns a list instead of one resolved account. Live checks on 2026-10-02 confirmed that the candidate search includes siloء (UID 283622404) when searching silo. See the player-search audit for provider comparisons and exact-name versus suggestion behavior.

• 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 RivalsClient(use_browser_fallback=True) as rd:
    player = rd.get_player(1970288503)

Errors and contributions

All package exceptions inherit from RivalsAPIError (also exported under the legacy RivalsDataError name). 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, RivalsTracker, Tracker.gg, NetEase, or Marvel. Keep request rates reasonable and respect the site's terms.

Multi-provider data

Existing functions combine public RivalsData, RivalsTracker, and Tracker.gg data with evidence-based selection of comparable values. New functions expose rank timelines, cosmetics, encounters, advanced career stats, global analytics, and community listings. Live Custom-game detection is not currently supported by the investigated public sources. See provider integration for examples, source semantics, and browser setup. Use RivalsClient(enrich=False) for RivalsData-only behavior.

Documentation

• Release notes and migration guide
• Provider integration and data-selection rules
• Research findings and unsupported features
• RivalsTracker API audit
• Tracker.gg API audit
• Observed RivalsData endpoints