lookup_hero

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

Discloses that backend silently drops unrecognized names and that this tool prevents that. Describes return fields (id, displayName, shortName, game) and error with did_you_mean. No contradictions; no annotations provided.

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?

Concise yet complete: one-sentence summary, usage guideline, technical details, and structured Args/Returns. Every sentence adds value. Front-loaded with purpose.

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?

Given no output schema, description explains return structure and error case. Covers game parameter values, silent drop behavior, and use case. Fully addresses tool complexity.

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 has 0% description coverage, so description carries full burden. It describes 'query' as 'A hero name, alias, or short name' and 'game' as 'dota2 (default) or deadlock'. Adds concrete meaning beyond raw schema.

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?

Description clearly states it normalizes a hero name/alias/shortName to canonical identity. Gives examples ('am', 'anti mage'). Distinguishes from sibling prediction tools by positioning it as a preprocessing step before calling them.

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?

Explicitly says to use this tool to turn messy input into exact key expected by prediction tools. Emphasizes always normalizing first because backend silently drops unrecognized names. Does not mention when not to use or alternatives.

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