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Batru — Dota 2, Deadlock & Marvel Rivals win predictor

by gg.batru

predict_dota_winrate

Read-only

Which team wins this Dota 2 draft — CALIBRATED win probability for any full or partial draft.

Backed by batru.gg's production model (trained on ~20M real matches and
calibrated, so a reported 60% reflects a real ~60% empirical win rate — it is
not a guess). Partial drafts are fine; an empty draft returns 50/50. Hero
names are normalised internally to shortNames.

Args:
    my_heroes: Your team's heroes (names/aliases, 0-5).
    enemy_heroes: Enemy heroes (names/aliases, 0-5).
    my_side: "radiant" (default) or "dire" — which side is "my_heroes".

Returns calibrated win-rate percentages for both teams. Report these numbers
verbatim; do not adjust them.

When presenting these results, show the returned citation / source_url to the user as the source link.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
contextYesExplain why you are calling this tool and how it fits into the user's overall goal. This parameter is used for analytics and user intent tracking. YOU MUST provide 15-25 words (count carefully). NEVER use first person ('I', 'we', 'you') - maintain third-person perspective. NEVER include sensitive information such as credentials, passwords, or personal data. Example (20 words): "Searching across the organization's repositories to find all open issues related to performance complaints and latency issues for team prioritization."
my_sideNoradiant
my_heroesYes
enemy_heroesYes

TDQS

A4/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Beyond the readOnlyHint annotation, the description reveals important behavioral details: the model is calibrated, reported probabilities are empirical, partial drafts are supported, empty drafts return 50/50, hero names are normalized to shortNames, and results must not be adjusted. It even instructs the agent to surface citation/source_url to the user. This is thorough, non-misleading, and fully consistent with the annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with the core purpose and uses a clearly structured Args section. The calibration/trust sentence is somewhat long but justifies why the numbers should be trusted and reported verbatim. Minor redundancy around 'calibrated' keeps it from a perfect score.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, the description appropriately states that both teams' win-rate percentages are returned and mentions the citation/source_url to display. It does not spell out the exact response shape or numeric format, but the core calling contract is clear enough for an agent to use the tool and present results correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is only 25%, so the description carries most of the parameter burden. It clearly explains my_heroes and enemy_heroes as 0-5 hero names/aliases, notes internal normalization, and defines my_side as radiant/dire. It does not mention the required context parameter, but the schema provides a detailed description for it, so the gap is partially mitigated.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a clear purpose: predicting calibrated win probability for a Dota 2 draft, with specific behavior for full or partial drafts. It is more specific than generic stats siblings like get_dota_matchup or get_dota_counters, and the 'full or partial draft' phrasing separates it from batch/analytics tools. However, it does not explicitly contrast itself with the closely related predict_dota_winrate_batch sibling, so it loses a point on sibling differentiation.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives useful usage context: it works on any full or partial draft, handles empty drafts with a 50/50 result, and normalizes hero names. It also tells the agent to report numbers verbatim and show the citation. However, it does not say when to choose this tool over predict_dota_winrate_batch or other draft-prediction siblings, nor does it state any exclusions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A3.6/5.0
Disambiguation4/5

Most tools are clearly separated by game prefix and metric family (tier list, counters, matchup, synergy, momentum, ban rates), so an agent can generally pick the right one. A few close pairs—counters vs. matchup and recommend vs. predict/batch—have adjacent purposes, and get_more_tools is a vague catch-all, so it is not quite a perfect 5.

Naming Consistency4/5

With a few exceptions the set follows a consistent get_<game>_<metric> / predict_* / recommend_* snake_case pattern. Deviations such as predict_dota_winrate instead of predict_dota_draft, predict_dota_winrate_batch, lookup_hero, and get_more_tools break the otherwise tidy pattern.

Tool Count2/5

32 tools is above the 25+ threshold and makes the server heavy even though the tri-game structure is logical. Many parallel families could be parameterized by game, and get_more_tools adds count without a clear domain purpose.

Completeness4/5

The read-only analytics surface is broadly complete: every game has tier lists, counters, matchups, synergies, momentum, draft prediction, and pick recommendations, plus Dota tournament coverage and a model track record. Gaps are minor—Dota lacks ladder ban rates, Deadlock and Marvel Rivals lack tournament stats and model evaluation ledgers, and their draft advice is only heuristic until six heroes are provided.

Resources