GPU Economy - cloud GPU prices

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

Annotations already establish this as a read-only operation, so the bar is lower; the description still adds real behavior: results are sorted cheapest first, priced in USD per GPU-hour, and carry source page and read timestamp per quote, plus the 'live quotes only' constraint. It does not mention rate limits or cache/staleness windows beyond the live-only claim.

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-loaded with the resource and scope, then the ordering/unit and the freshness constraint. Every clause carries information; nothing is padded.

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?

With no output schema, the description does the work of describing returns: quote, source page, read time, ordering and unit. That is nearly sufficient, though it gives no detail on empty-result behavior or whether coverage across providers is exhaustive.

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 gpu, limit and market are already fully documented, including enum values and defaults. The description's 'cheapest first' and 'USD per GPU-hour' restate what the schema already conveys, so baseline 3 applies.

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 with scope: 'Current listed rental prices for one GPU model across providers'. The 'one GPU model across providers' framing implicitly separates it from siblings like provider_prices (one provider, many GPUs) and price_index, but no sibling is named explicitly.

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?

Usage context is implied by 'current' and 'live quotes only', but there is no explicit when-to-use/when-not guidance and no named alternative among list_gpus, price_index, provider_prices or recent_price_changes. An agent must infer routing from scope alone.

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?

Annotations already declare readOnlyHint=true and openWorldHint=false, so safety is covered. The description adds real value by disclosing the return shape (id, VRAM, provider count, median rate card) since there is no output schema. It does not mention list size or pagination, which is a minor gap for a catalog 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?

Two sentences, front-loaded with scope and payload, ending with the chaining instruction. Every clause earns its place with no filler.

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?

With no output schema and no parameters, the description carries the return-value load and does so by naming each field plus units (USD per GPU-hour). An agent can invoke it and parse the result without further documentation.

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?

The tool takes zero parameters, so the baseline is 4; there is nothing for the description to disambiguate. It correctly avoids inventing parameter semantics that the empty schema does not support.

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 (list) and resource (GPU models tracked) and enumerates the returned fields (id, VRAM, provider count, median on-demand rate). It also names the sibling gpu_prices, so an agent can separate it from price_index/provider_prices without opening a schema.

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?

'Use the id with gpu_prices' gives explicit downstream routing, which is the key usage decision for this tool. It stops short of stating when NOT to use it (e.g. when you already have ids), so it is strong context rather than full when/when-not guidance.

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?

Annotations already establish this is a safe read (readOnlyHint=true, openWorldHint=false). Beyond that, the description discloses the temporal scope (this week plus the last eight weeks), the aggregation basis (median listed on-demand), and that base-100 index values and source receipts are included - meaningful return-shape context given no output schema exists.

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?

A single front-loaded sentence that leads with the resource name and layers in the key scoping details. No filler, though 'with the receipts' is a slightly vague tail that could be tightened.

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?

With no parameters, no output schema, and annotations covering the safety profile, the description carries the burden of explaining return content - and it does: metric, aggregation, time window, index, and sources. The only real gap is routing guidance relative to sibling price tools, which is a usage concern more than completeness.

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?

The tool takes zero parameters, so there is nothing to document and the schema cannot be a source of ambiguity. Baseline of 4 applies; the description correctly implies no inputs are needed.

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 names a specific resource (the weekly Cloud GPU Price Index) and defines its contents precisely: median listed on-demand H100 SXM rate across providers, plus a base-100 index. An agent knows exactly what data this returns, though it never distinguishes this tool from siblings like gpu_prices or recent_price_changes that likely offer overlapping price data.

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?

There is no statement of when to use this tool versus the four siblings (gpu_prices, list_gpus, provider_prices, recent_price_changes). The weekly-index framing hints at trend analysis, but the agent must infer whether to call this or a sibling for a given price question; no alternatives or exclusions are named.

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?

Annotations declare readOnlyHint=true and openWorldHint=false, so safety is covered. The description adds real behavioral context beyond that: results are live quotes (not cached/historical), denominated in USD per GPU-hour, and each entry carries a source page and read timestamp. No return format or pagination detail is given, but the additions are meaningful.

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 tight sentences with zero filler; scope, resource, units, and freshness constraint are all front-loaded.

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?

With no output schema, the description usefully characterizes what comes back (per-GPU-hour USD price, source page, read time) and flags the data as live. It largely compensates for the missing output schema, though it says nothing about result volume or ordering.

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 a single required 'provider' parameter already documenting accepted ids (runpod, lambda, coreweave, aws, ...). The description's parenthetical examples duplicate the schema rather than extending it, so the baseline 3 applies.

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 resource (current listed rental prices), scope (one provider), and units (USD per GPU-hour). The 'at one provider' qualifier implicitly scopes it against a broader sibling like gpu_prices, but no sibling is named or contrasted explicitly.

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 implies you call this when you want current prices for a single named provider, and 'Live quotes only' narrows it to real-time data. However, it never states when to prefer it over gpu_prices, price_index, or recent_price_changes, leaving the agent to infer routing.

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?

Annotations already establish this as a safe read (readOnlyHint=true, openWorldHint=false), yet the description adds real behavior: exclusion of quotes that returned to their starting figure, exclusion of marketplace floors by default, ordering newest-first, and one row per quote. That is meaningful context beyond the safety profile.

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 dense sentences, front-loaded with the core purpose and the row semantics before the optional filtering clause. Slightly run-on, but no filler and every clause carries 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?

With no output schema, the description usefully describes the return shape (prior figure, current figure, change count, ordering) and the exclusion rules. Minor gaps remain around pagination/limit behavior, but nothing critical to invoking the tool correctly is missing.

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 all four parameters are already documented, making 3 the baseline. The description reinforces that gpu/provider are optional single-value filters and that marketplaces are excluded 'unless asked for', but adds no format or syntax detail beyond the 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?

States a specific verb+resource with the exact scope: 'listed prices that changed in the last 30 days' and the shape of each row. It implicitly differs from sibling tools like gpu_prices/provider_prices (point-in-time prices vs. changes), but never names or contrasts them, so it falls short of a 5.

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?

Usage is only implied: 'Optionally for one GPU or one provider' hints at filtering but gives no when-to-use guidance, no mention of when to reach for gpu_prices or price_index instead, and no stated prerequisites.

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