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

Annotations already cover safety (readOnlyHint true, destructiveHint false, openWorldHint true), and the description adds real value beyond them: data provenance (computed from 140M+ matches by a production pipeline, 'measured numbers, not model guesses') and the exact return shape plus the fetch handoff. It does not mention result-count caps, ranking/scoring behavior, or data freshness, which are the remaining behavioral unknowns for a search endpoint.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loaded with the core action and scope, then examples, then a return-value contract — a sensible order. The 'Args: query' block largely restates the schema's own parameter documentation, and the 'measured numbers, not model guesses' line is promotional, so a few sentences don't fully earn their place, but nothing is confusing or bloated.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

No output schema exists, and the description compensates by specifying the return shape ({results: [{id, title, url}]}) and how to continue to fetch — the key information for a search tool. The notable omission is the mandatory `context` parameter, which an agent reading only prose would not know it must supply, though the schema documents it thoroughly.

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

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

Schema description coverage is 100%, so baseline would be 3; the description earns an extra point by giving four concrete natural-language query examples ('who counters axe dota', 'marvel rivals mantis duo') that demonstrate the expected phrasing style, which the schema does not convey. The required `context` analytics parameter is not mentioned at all in the description, leaving that gap to the schema alone.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource ('Search batru.gg's live competitive datasets') and enumerates the game domains it covers, so the agent knows exactly what corpus is being queried. It also names the follow-up tool ('Pass a result's `id` to `fetch`'), which distinguishes it as a discovery layer. However, the listed content types (hero counters, tier lists, ban rates) overlap directly with structured siblings like get_counters, get_ban_rates and get_tier_list without stating the difference.

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

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

Gives concrete triggering contexts — hero counters, synergies/duos, patch tier lists, ban rates, pro tournament results, model ledger — which is a clear 'when to use'. The chaining instruction to fetch by `id` clarifies its place in the workflow. It stops short of naming when NOT to use it versus the specialized get_* siblings that return the same class of data in structured form.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.