Drop Index

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

No annotations are provided, so the description bears full responsibility. It discloses that the tool polls indie shops and returns indices for restocking, dropped, or low stock items. This is adequate but lacks details like data freshness, rate limits, or behavior on invalid niches (though the niche constraint is given).

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?

Two sentences front-load the purpose and contextual information (niche values). Every sentence is necessary and efficient with zero waste.

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?

For a single-parameter tool with no output schema, the description is fairly complete: it explains the output meaning and parameter constraint. It could mention potential edge cases (e.g., no data for niche) but remains sufficient.

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 coverage is 100% with the niche parameter fully described. The description repeats the list of valid niches, adding no extra semantics beyond what the schema already provides. Thus it meets the baseline without enhancement.

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?

The description uses a specific verb ('Return') and clearly identifies the resource ('live restock index for a niche'). It distinguishes from sibling tools by describing its unique function (current restock signals) vs. list_niches (which lists niches) and will_it_restock (predictions).

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?

The description states when to use ('to see real-time availability signals') and implicitly contrasts with sibling tools by describing its output. However, it does not explicitly say when not to use it or name alternatives, leaving room for slight ambiguity.

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

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

No annotations provided, so description carries full burden. Describes basic behavior but does not disclose details like pagination, caching, or side effects. Given simplicity, it is minimally adequate.

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?

Two sentences, no wasted words, front-loaded with action. Extremely concise while conveying essential information.

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, no annotations, and zero parameters, the description is mostly complete. Could mention the return format, but not strictly necessary for such a simple tool.

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?

Zero parameters, so schema provides no param info. Description adds meaning by stating the tool's purpose. Baseline for 0 params is 4.

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?

Clearly states the verb 'List' and the resource 'collectible niches', and explains its purpose to pick a valid niche value. Distinguishes from sibling tools which likely serve different purposes.

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?

Provides clear context: 'Use to pick a valid nickel value.' Implicitly indicates when to use, but does not mention when not to use or alternative tools.

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

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

No annotations are provided, so the description carries the full burden. It discloses return values including a 'no history yet' case, indicating transparency about data limitations. However, it does not mention whether the tool is read-only, any permissions needed, or rate limits. The behavioral disclosure is adequate but not comprehensive.

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?

The description is a single sentence that immediately states the core purpose, then lists return values. It is concise, front-loaded, and contains no unnecessary words. Every part contributes to understanding.

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?

The tool has two parameters with full schema coverage, no output schema, and a simple prediction task. The description covers what the tool does and what it returns (stats, probability, verdict, or no history). It is complete enough for an agent to use effectively, though it could specify output format slightly more.

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%, with clear descriptions for both parameters ('Niche key (see list_niches)' and 'Item name to predict, e.g. Cynthia's Garchomp'). The description adds minimal extra meaning beyond restating the prediction purpose. Baseline 3 is appropriate since the schema already provides good definitions.

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?

The description states a specific verb 'predict' and resource 'whether a specific item will restock', listing return values (stats, probability, verdict). It clearly distinguishes from sibling tools like 'list_niches' which lists niches, and 'drop_index' which is unrelated.

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?

The description provides explicit context: 'Use before paying a resale premium.' This tells the agent when to use the tool. While it doesn't specify when not to use or alternatives, the context is clear and sufficient for a simple prediction tool.

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