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batrugg
by batrugg

predict_deadlock_draft

Predict calibrated win probabilities for any Deadlock 6v6 draft lineup. Enter hero names for both teams to get real, empirically calibrated win-rate percentages.

Instructions

Predict the CALIBRATED win probability for a Deadlock 6v6 draft.

Backed by batru.gg's Deadlock production model. Provide 6 heroes per team (names are normalised to Deadlock hero ids internally). A reported 60% reflects a real ~60% empirical win rate — it is calibrated, not a guess.

Args: team0_heroes: Team 0's 6 heroes (names/aliases). team1_heroes: Team 1's 6 heroes (names/aliases).

Returns calibrated win-rate percentages for both teams. Report verbatim.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
team0_heroesYes
team1_heroesYes
Behavior4/5

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

No annotations are provided, so the description carries the full burden. It explains that the prediction is 'calibrated' (not a guess) and instructs to 'Report verbatim' for the returned win-rate percentages. It also notes internal hero name normalization. This provides good behavioral context, though rate limits or error cases are not discussed.

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

Conciseness5/5

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

The description is concise and well-structured: a lead sentence stating the core function, a supporting sentence about calibration and model source, then explicit parameter documentation, and a final note on return values. Every sentence adds value without redundancy.

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?

Given the tool's complexity (6 heroes per team, calibrated model), the description covers purpose, input requirements, and output nature. It lacks details on error handling or invalid inputs, but for a straightforward prediction tool, it is largely complete.

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 coverage is 0%, so parameters lack descriptions in the schema. The description explicitly documents both parameters (team0_heroes, team1_heroes) as 'Team 0's 6 heroes (names/aliases)' and similarly for team1, adding meaning beyond the schema's array type. This compensates well for the low coverage.

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 it predicts 'calibrated win probability for a Deadlock 6v6 draft', using specific verb 'predict' and resource 'win probability'. It distinguishes itself from sibling tools (e.g., predict_dota_winrate) by specifying Deadlock context.

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 mentions 'Provide 6 heroes per team' and 'Backed by batru.gg's Deadlock production model', implying use for Deadlock drafts. However, it does not explicitly state when not to use or compare with alternatives, leaving usage context implied.

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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