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Batru — Dota 2, Deadlock & Marvel Rivals win predictor

by gg.batru

lookup_hero

Read-only

Normalise a hero name/alias/shortName to its canonical identity.

Use this to turn messy user input ("am", "anti mage", "Anti-Mage") into the
exact key batru.gg expects before calling the prediction tools. The backend
SILENTLY DROPS hero names it doesn't recognise, so always normalise first.

Args:
    query: A hero name, alias, or short name.
    game: "dota2" (default), "deadlock", or "marvel-rivals".

Returns {id, displayName, shortName, game} for the best match, or an error
with `did_you_mean` candidates if nothing matches.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
gameNodota2
queryYes
contextYesExplain 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."

TDQS

A4.4/5.0
Behavior4/5

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

Annotations indicate readOnlyHint, openWorldHint, and destructiveHint. The description adds key behavioral context: the backend silently drops unrecognized names, and the tool returns either a match with structured fields or an error with suggestions. This goes beyond what annotations provide.

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 structured with clear sections (Args, Returns) and uses straightforward language. Every sentence contributes to understanding, with no fluff. It could be slightly more concise, but the detail is warranted given the tool's critical role.

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 the tool's simplicity (3 parameters, no output schema), the description covers all necessary aspects: purpose, usage, parameter meanings, return structure, and error handling. It is fully adequate for an AI agent to successfully invoke the tool.

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?

With only 33% schema description coverage, the description compensates by explaining the 'query' parameter (hero name, alias, or short name) and the 'game' parameter (enumerated options with default). However, it does not detail the 'context' parameter beyond what the schema provides, missing a chance to clarify its purpose.

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 uses specific verbs ('normalise') and clearly identifies the resource ('hero name/alias/shortName to canonical identity'). It provides concrete examples of messy input and explicitly states it is the prerequisite for prediction tools, distinguishing it from sibling tools that perform analysis or predictions.

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 tells when to use the tool ('before calling the prediction tools') and why it's necessary ('backend silently drops hero names it doesn't recognise'). It offers a clear context, though it does not explicitly state when not to use it (e.g., if the name is already canonical).

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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TDQS

A3.6/5.0
Disambiguation4/5

Most tools are clearly separated by game prefix and metric family (tier list, counters, matchup, synergy, momentum, ban rates), so an agent can generally pick the right one. A few close pairs—counters vs. matchup and recommend vs. predict/batch—have adjacent purposes, and get_more_tools is a vague catch-all, so it is not quite a perfect 5.

Naming Consistency4/5

With a few exceptions the set follows a consistent get_<game>_<metric> / predict_* / recommend_* snake_case pattern. Deviations such as predict_dota_winrate instead of predict_dota_draft, predict_dota_winrate_batch, lookup_hero, and get_more_tools break the otherwise tidy pattern.

Tool Count2/5

32 tools is above the 25+ threshold and makes the server heavy even though the tri-game structure is logical. Many parallel families could be parameterized by game, and get_more_tools adds count without a clear domain purpose.

Completeness4/5

The read-only analytics surface is broadly complete: every game has tier lists, counters, matchups, synergies, momentum, draft prediction, and pick recommendations, plus Dota tournament coverage and a model track record. Gaps are minor—Dota lacks ladder ban rates, Deadlock and Marvel Rivals lack tournament stats and model evaluation ledgers, and their draft advice is only heuristic until six heroes are provided.

Resources