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batru-mcp

batru-mcp

by batrugg
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

predict_dota_winrate

Predict the calibrated win rate for a Dota 2 draft using a production model trained on millions of matches. Supports partial drafts and returns accurate percentages for both teams.

Instructions

Predict the CALIBRATED win rate for a Dota 2 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.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "my_heroes": "<my_heroes>",
      "enemy_heroes": "<enemy_heroes>"
    },
    "name": "predict_dota_winrate"
  }
}

Input Schema

NameRequiredDescriptionDefault
my_sideNoradiant
my_heroesYes
enemy_heroesYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.2

TDQS

A4.8/5.0
Behavior5/5

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

No annotations provided, so description carries full burden. It discloses calibration (model is calibrated so reported numbers are empirical), that hero names are normalised, partial drafts supported, and that returns should be reported verbatim. This is thorough behavioral context.

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: a clear opening sentence, then a paragraph of important context, and a bullet-like Args section. Every sentence adds value; no redundancy or fluff.

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?

Given 3 parameters, no output schema, and no annotations, the description fully covers inputs, behavior, calibration, and return instructions. It answers likely agent questions about partial drafts, empty drafts, and output handling.

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 0%, but the description explains each parameter in detail: my_heroes and enemy_heroes are arrays of hero names/aliases (0-5), my_side defaults to 'radiant' and specifies which side is my_heroes. This adds full meaning beyond the schema's minimal title and type.

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 'Predict the CALIBRATED win rate for a Dota 2 draft.' It specifies the exact action (predict), resource (Dota 2 draft), and adds nuance (calibrated). Siblings like lookup_hero or recommend_dota_pick are distinct, making this tool's purpose unambiguous.

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

Usage Guidelines4/5

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

The description explains when to use the tool (e.g., for draft win rate prediction), notes that partial drafts are fine and empty draft returns 50/50. However, it does not explicitly contrast with siblings or state when not to use it. The sibling names provide context but the description itself could be more explicit.

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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