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Batru — Competitive Game Intelligence for AI Agents

by gg.batru
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get_matchup

Read-only

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".
    hero: Your hero.
    opponent: The enemy hero.

Report the number verbatim.

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

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
gameYesWhich game: "dota2", "deadlock" or "marvel-rivals". Required — hero names alone do not identify the game.
heroYesThe hero you are asking about (any alias; normalised internally).
contextYesExplain in 15-25 words, in third person, why this tool is called and how it supports the user's goal. For analytics only. You MUST describe only the abstract purpose of the tool call. NEVER include, repeat, paraphrase, or infer personal, sensitive, or identifying information from the user request or tool results, including names, emails, phone numbers, IPs, IDs, or credentials. You MUST generalize specific entities into roles such as "a user", "the customer", or "an account". Example: "Retrieving a customer's recent orders to investigate a billing issue and help support determine the appropriate resolution."
opponentYesThe opposing hero (any alias; normalised internally).

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed3 schema fields changed
    • addedInput schema / properties / game / description
      Added value: +"Which game: \"dota2\", \"deadlock\" or \"marvel-rivals\". Required — hero names alone do not identify the game."
    • addedInput schema / properties / hero / description
      Added value: +"The hero you are asking about (any alias; normalised internally)."
    • addedInput schema / properties / opponent / description
      Added value: +"The opposing hero (any alias; normalised internally)."
  2. Changed1 schema field changed
    • changedInput schema / properties / context / description
      Previous value: -"Explain 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.\""New value: +"Explain in 15-25 words, in third person, why this tool is called and how it supports the user's goal. For analytics only. You MUST describe only the abstract purpose of the tool call. NEVER include, repeat, paraphrase, or infer personal, sensitive, or identifying information from the user request or tool results, including names, emails, phone numbers, IPs, IDs, or credentials. You MUST generalize specific entities into roles such as \"a user\", \"the customer\", or \"an account\". Example: \"Retrieving a customer's recent orders to investigate a billing issue and help support determine the appropriate resolution.\""
  3. Added

TDQS

A4.1/5.0
Behavior4/5

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

Annotations already declare readOnlyHint/openWorldHint/safe, and the description adds genuine behavioral disclosures beyond them: the result includes sample size, names are normalised internally, the number must be reported verbatim, and the citation/source_url must be shown to the user. It does not cover edge cases like missing data or tiny sample sizes, which keeps it at 4.

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 compact and front-loaded with its most defining sentence ('One hero vs one opponent — the measured head-to-head win rate...'). The verbatim and citation instructions earn their place as output-contract guidance. However, the Args section is redundant with the fully-covered schema and omits context, so not every sentence pulls its weight.

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?

For a 4-required-param tool with no output schema, the description covers the essential return contract (the number, sample size, citation/source_url to display) and the normalization behavior. The only real gap is that the description's own arg summary omits the context parameter, though the schema fully documents it. Overall adequate-to-strong for a simple read-only lookup.

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

Parameters3/5

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

Schema description coverage is 100%, so the baseline is 3 even with no parameter info in the description. The Args section largely restates the schema (game enum, hero, opponent) and adds no new meaning; 'names normalised internally' appears in both. Notably the Args list omits the required context parameter, so the description does not compensate for or even acknowledge the full parameter set. 3 is appropriate.

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 first line states a specific verb and resource: one hero vs one opponent, the measured head-to-head win rate from real games in three named games. The explicit 'measured/direct matchup from real games' framing cleanly separates it from predictive siblings like predict_winrate, and the 'does X beat Y?' scope distinguishes it from get_synergy and get_counters. The title and description align well.

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 gives clear context for when to call it — 'the direct matchup number from real games' signals this is the empirical/measured tool for head-to-head questions. It does not name alternatives explicitly or state when-not-to-use, so it stops short of a 5, but the 'measured vs predicted' contrast is a strong practical routing signal.

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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