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Batru — Competitive Game Intelligence for AI Agents

by gg.batru
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get_tournament_stats

Read-only

TI / EWC pro tournament stats — actual most-picked, most-banned and win rates per hero.

Real recorded results from the official drafts of every game at a covered
pro Dota 2 event (The International, Esports World Cup, ...) — small-sample
records, not calibrated probabilities. Answers "most picked / banned /
contested at TI", "who won", "group standings".

Args:
    event: Event slug, e.g. "ti-2026" (default), "ti-2025",
        "esports-world-cup-2026".
    limit: Max heroes per table (default 15).
    game: Only "dota2" today.

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

Input Schema

NameRequiredDescriptionDefault
gameNoOnly "dota2" has pro tournament coverage today.dota2
eventNoEvent slug, e.g. "ti-2026" (The International 2026) or "ewc-2026" (Esports World Cup).ti-2026
limitNoMaximum number of rows to return.
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
    • changedInput schema / properties / game / enum
      Previous value: -[
-  "dota2",
-  "deadlock",
-  "marvel-rivals"
-]New value: +[
+  "dota2",
+  "deadlock",
+  "marvel-rivals",
+  "lol"
+]
  2. Changed3 schema fields changed
    • addedInput schema / properties / event / description
      Added value: +"Event slug, e.g. \"ti-2026\" (The International 2026) or \"ewc-2026\" (Esports World Cup)."
    • addedInput schema / properties / game / description
      Added value: +"Only \"dota2\" has pro tournament coverage today."
    • addedInput schema / properties / limit / description
      Added value: +"Maximum number of rows to return."
  3. 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.\""
  4. Added

TDQS

A4.1/5.0
Behavior4/5

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

Annotations already declare readOnly/openWorld/non-destructive, so the bar is lower. The description still adds real value: it warns that samples are small and not probabilities, and it instructs the agent to surface the returned `citation` / `source_url` to the user — an output-handling requirement annotations cannot express.

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 purpose, the small-sample caveat and the citation instruction are all front-loaded and each sentence earns its place. The trailing Args block largely restates the schema and is the only redundant portion.

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 read-only query tool with no output schema, it covers scope, data provenance, sample-size caveat, and required output attribution. It does not state whether results are paginated or how many rows the hero tables can contain beyond `limit`, but nothing an agent needs to call it correctly is missing.

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 3 is the baseline. The description does add example slugs, but it diverges from the schema: the description offers 'esports-world-cup-2026' while the schema says 'ewc-2026', and it claims game supports only 'dota2' while the enum lists four values — a mild inconsistency rather than added clarity.

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 opening line names the exact resource and scope: pro tournament stats for TI/EWC with most-picked, most-banned and win rates per hero. It also distinguishes itself from sibling prediction tools by stating the data is 'real recorded results... not calibrated probabilities', which separates it from predict_winrate and get_model_track_record.

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 concrete triggering questions ('most picked / banned / contested at TI', 'who won', 'group standings'), which is clear usage context. It stops short of naming alternatives explicitly (e.g. get_ban_rates or get_tier_list for non-tournament data), so there is no explicit when-not to use it.

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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