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Batru — Competitive Game Intelligence for AI Agents

by gg.batru
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Read-only

Search batru.gg's live competitive datasets for Dota 2, Deadlock and Marvel Rivals.

Finds the right document for a question about hero counters, hero synergies
/duos, current-patch tier lists, ban rates, pro tournament results (TI,
Esports World Cup) or the prediction model's public evaluation ledger.
Data behind every document is computed from 140M+ real matches by
batru.gg's production pipeline — measured numbers, not model guesses.

Args:
    query: Free-text search, e.g. "who counters axe dota", "best deadlock
        heroes", "marvel rivals mantis duo", "ti 2026 most banned".

Returns {results: [{id, title, url}]}. Pass a result's `id` to `fetch` to
get the full document.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesFree-text search over Batru's live esports data (hero names, matchups, tournaments, patches).
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."

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedInput schema / properties / query / description
      Added value: +"Free-text search over Batru's live esports data (hero names, matchups, tournaments, patches)."
  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

A4.4/5.0
Behavior4/5

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

Annotations already mark readOnlyHint=true and destructiveHint=false; the description adds that results are {results: [{id, title, url}]}, that data is computed from 140M+ real matches, and that the agent should call `fetch` with an id. It does not disclose pagination or result limits, but for a read-only search tool this is adequate.

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 front-loaded with purpose, followed by a compact topic list, a one-line data-provenance point, and a clear Args/Returns section. Every sentence contributes either scope, workflow, or return-format information, with no filler.

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?

It covers the search domain, data source, query examples, return shape, and the follow-up call to `fetch`; with no output schema this return description is necessary and present. It does not mention result ordering/count or error behavior, and it leaves `context` to the schema, so it is good but not exhaustive.

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 100%, so the schema carries most of the parameter burden; the description still adds value with concrete query examples ('who counters axe dota', 'best deadlock heroes') and clarifies query is free-text. The Args section omits the required `context` parameter, but the schema fully documents it, so this is a minor gap.

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 opens with a specific verb+resource ('Search batru.gg's live competitive datasets') and names the covered games and topics. It differentiates itself from the get_* siblings by describing document-level search returns and telling the agent to pass a result id to `fetch`, which separates it from direct-data tools.

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?

It gives clear when-to-use context by enumerating query intents: counters, synergies, tier lists, ban rates, tournament results, and the model's evaluation ledger. It does not explicitly list exclusions or name alternative siblings such as get_counters or get_ban_rates for direct stats, so it misses the full 5.

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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