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Batru — Dota 2, Deadlock & Marvel Rivals win predictor

by gg.batru

predict_dota_winrate_batch

Read-only

Score MANY candidate next-picks for a Dota 2 draft in ONE call.

Use this instead of looping predict_dota_winrate over candidates: the whole
batch runs as a single model invoke server-side, so it is much faster and
cheaper for everyone. For each candidate you get the CALIBRATED win rate
your team would have after adding it against the given enemy draft, plus
the delta vs the draft without it. Same production model as
predict_dota_winrate (a reported 60% reflects a real ~60% empirical win
rate). Hero names are normalised internally.

Args:
    my_heroes: Heroes your team has already picked (names/aliases, 0-4).
    enemy_heroes: Enemy heroes (names/aliases, 0-5).
    candidates: Candidate heroes to evaluate (names/aliases, 1-150 — a
        full-roster sweep fits in one call).
    my_side: "radiant" (default) or "dire" — which side is "my_heroes".

Returns {baseline_win_rate_pct, candidates:[{hero, shortName, win_rate_pct,
delta_pp}]} sorted best-first; delta_pp = win_rate_pct minus the baseline
(the same draft without that candidate). Report numbers verbatim.

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
candidatesYes
enemy_heroesYes

TDQS

A4.9/5.0
Behavior5/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, but the description adds valuable behavioral context: a single server-side model invoke, output calibration ('a reported 60% reflects a real ~60% empirical win rate'), internal hero name normalization, and the return structure with baseline and delta. This goes well beyond the annotations and provides deep transparency about how the tool behaves.

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 well-organized with a bold upfront summary, Args, Returns, and usage notes. It is generally concise given the complexity. However, the final sentence about 'citation' / 'source_url' seems extraneous and inconsistent with the stated return object (which does not include those fields), slightly undermining focus and tightness.

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

Completeness5/5

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

Despite no output schema, the description fully covers return values, meaning of delta_pp, calibration context, and normalization. It also addresses performance, side semantics, and candidate limits. This is complete for an agent to invoke and interpret results correctly, leaving no major gaps.

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

Parameters5/5

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

Schema coverage is only 20%, so the description carries the burden. The Args section adds critical semantics beyond the schema: my_heroes (0-4), enemy_heroes (0-5), candidates (1-150, full-roster sweep), my_side default ('radiant'). It also clarifies what each parameter represents in the draft context. This fully compensates for the sparse schema.

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

Purpose5/5

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

The description clearly states the tool's purpose with a specific verb and resource: 'Score MANY candidate next-picks for a Dota 2 draft in ONE call.' It explicitly distinguishes itself from the sibling tool predict_dota_winrate by emphasizing batch processing and one-call efficiency, making the tool's unique scope unambiguous.

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

Usage Guidelines5/5

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

Explicit when-to-use guidance is provided: 'Use this instead of looping predict_dota_winrate over candidates' and explains the benefit ('much faster and cheaper for everyone'). It also clarifies that it uses the same production model, giving a clear alternative and rationale, which satisfies usage guidance fully.

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