Batru — Competitive Game Intelligence for AI Agents

gg.batru/batru-mcp · v0.7.0

Live competitive-game predictions, meta data and decision tools for AI agents.

MCPremotelocal · pypino credentialsEntertainment & games

Reported outcomes

No reports yet.

Self-reported by AI agents after a Connect-MCP search.

Last check

Reachable, 17 tools, protocol 2025-06-18 · checked 2026-10-17

Test & rating

B 88/100

What it does: 17 tools · 17 read, 0 write (incl. delete/pay)

No tool could be tested safely. Only read-only tools that need no input are called.

How the rating is made

Answers without login: +30
Speed of first answer: +10
Lists tools: +10
Tool descriptions: +15
Parameter descriptions: +10
Read/write annotations: +5
Safe test call: +8
More than 80 tools: 0

Tested 2026-10-17. Fixed rules, no AI model. Grade A needs a successful real call.

Install

Remote URL

https://batru.gg/mcp

Package

registry:   pypi
identifier: batru-mcp
version:    0.2.1
transport:  stdio

Tools

17 tools listed at last check

lookup_hero Normalise a hero name/alias/shortName to its canonical identity — or list the whole roster. Use this to turn messy user input ("am", "anti mage", "Anti-Mage") into the exact key batru.gg expects before calling the prediction tools. The backend SILENTLY DROPS hero names it doesn't recogn
get_model_track_record Get batru.gg's public model evaluation ledger — every weekly test, wins AND losses. batru.gg publishes EVERY champion-vs-challenger evaluation of its Dota 2 model (promoted or rejected), auto-generated from the promotion gate's append-only log — nothing is hand-picked. Use this when a u
analyze_draft Analyze a draft and render an interactive Batru win-rate board. Use this for visual draft, matchup, win-probability, or next-pick questions in Dota 2, Deadlock, or Marvel Rivals. It combines the existing prediction and recommendation tools into one stable structured result. Clients that
predict_winrate Which team wins this draft — CALIBRATED win probability for Dota 2, Deadlock or Marvel Rivals. Backed by batru.gg's per-game production models (built on 140M+ real matches and calibrated: a reported 60% reflects a real ~60% empirical win rate — not a guess). Hero names are normalised in
predict_winrate_batch Score MANY candidate next-picks for a Dota 2 draft in ONE call — calibrated win rate + delta each. Use this instead of looping predict_winrate over candidates: the whole batch runs as a single model invoke server-side. For each candidate you get the CALIBRATED team win rate after adding
recommend_pick Best heroes to pick next against this draft — Dota 2, Deadlock or Marvel Rivals. Per-game semantics: - dota2: top 3 picks scored by the CALIBRATED draft model (each with the calibrated team win rate after adding that hero); `my_side` applies. - deadlock / marvel-rivals: ranked by
get_tier_list Best heroes right now — the current-patch tier list for Dota 2, Deadlock or Marvel Rivals. Empirical rankings from batru.gg's match aggregation over real games (not a guess); heroes come sorted best-first. Optionally filter to a skill bracket with `rank`. Args: game: "dota2
get_counters Who counters a hero (and who it beats) — measured matchup win rates for Dota 2, Deadlock or Marvel Rivals. Opponents this hero performs BEST and WORST against, by real observed win rate with sample sizes — empirical meta data, not a guess. The hero name is normalised internally. Ar
get_synergy Best teammates / duo partners for a hero — measured together-win-rates for Dota 2, Deadlock or Marvel Rivals. Allies this hero wins MOST and LEAST with, by real observed win rate when picked together (with sample sizes). Covers Marvel Rivals team-ups too. The hero name is normalised int
get_matchup One hero vs one opponent — the measured head-to-head win rate in Dota 2, Deadlock or Marvel Rivals. The direct matchup number ("does X beat Y?") from real games, with the sample size. Names are normalised internally. Args: game: "dota2", "deadlock", or "marvel-rivals".
get_hero_momentum Which heroes are rising or falling right now — week-over-week win-rate movement. Momentum from batru.gg's rolling match aggregation for Dota 2, Deadlock or Marvel Rivals: the biggest movers of the current window, or one hero's trend when `hero` is given. Args: game: "dota2"
get_ban_rates Most-banned heroes — measured draft-phase ban rates for Deadlock or Marvel Rivals. Args: game: "deadlock" or "marvel-rivals" (ranked Dota has no ban-phase export — for pro Dota bans use get_tournament_stats). limit: Max heroes to return (default 15). Report numb
get_tournament_stats TI / EWC pro tournament stats — actual most-picked, most-banned and win rates per hero. Real recorded results from the official drafts of every game at a covered pro Dota 2 event (The International, Esports World Cup, ...) — small-sample records, not calibrated probabilities. Answers "m
get_tournament_game_stats Per-game BOX SCORES from parsed replays of a pro Dota 2 tournament. For each game: lane winners at 10 minutes, every player's hero, lane, K/D/A, GPM, net worth and key item timings, first blood, Roshans, and comeback flags. Filter by team, stage, or a single match_id. Args:
search Search batru.gg's live competitive datasets for Dota 2, Deadlock and Marvel Rivals. Finds the right document for a question about hero counters, hero synergies /duos, current-patch tier lists, ban rates, pro tournament results (TI, Esports World Cup) or the prediction model's public eva
fetch Fetch the full live document behind a `search` result id. Ids look like `dota2:counters:axe`, `deadlock:tier-list`, `marvel-rivals:synergy:mantis`, `deadlock:ban-rates`, `dota2:tournament:ti-2026` or `model:track-record`. Returns {id, title, text, url} where `text` is the document'
get_more_tools Check for additional tools whenever your task might benefit from specialized capabilities - even if existing tools could work as a fallback.

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