rentgpu GPU rental price market data

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

No annotations exist, so the description carries the burden and mostly does: it discloses the lookback window, the exact result fields, a sample result row, and a per-call price of $0.05 USDC. It omits behavior on unknown GPU names, whether limit may exceed 30, and the currency/unit conventions behind the price fields.

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?

One front-loaded sentence defines the resource and the returned fields, followed by a concrete example and a one-line cost note. Every element earns its place; the example does the heavy lifting for format without extra prose.

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 annotations, the description compensates well by enumerating the returned fields and showing a sample row. Remaining gaps are secondary: how to resolve valid GPU names and error/edge behavior.

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 0%, so the description must compensate, and the example (gpu='H100 80GB', limit=7) conveys both the GPU name format and that limit truncates the number of returned days. It does not name an authoritative source for valid gpu values, though list_gpu_models is a sibling that would serve that role.

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 an exact scope: a 30-day daily price series per GPU, enumerating the returned fields. An agent can immediately separate it from gpu_price (current snapshot) and market_movers (aggregate movers) without opening the 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?

The 30-day time-series framing implies when to pick this over gpu_price, and the worked example shows the intended call shape. However, it never explicitly names an alternative or states exclusions (e.g., 'for the current price use gpu_price').

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 must carry the full disclosure burden. It usefully adds the per-call cost ($0.05 USDC) and lists the output fields (platform, 30-day low/high/average, gap), but it does not explicitly state that the operation is read-only or mention rate limits.

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 front-loaded with the core purpose, followed by a concrete example that illustrates both input and output. It is efficiently sized with no extraneous content.

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 simple one-parameter query with no output schema, the description covers the return fields and billing model, which is sufficient. A modest gap remains: it does not direct users to list_gpu_models for valid GPU names, which could prevent input errors.

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 0%, so the description must compensate for the single 'gpu' parameter. The example 'gpu=\'RTX 4090\'' gives a format hint, but it does not specify case sensitivity, accepted model name lists, or validation rules.

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 and resource: querying the current lowest price of a given GPU across all platforms, and it lists the returned metrics. It distinguishes itself from a historical tool by emphasizing '现在的' (current), but it does not explicitly contrast with siblings like gpu_history or list_gpu_models.

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 example demonstrates one usage, but there is no explicit guidance on when to use this tool versus gpu_history or other siblings. No exclusions or alternative conditions are stated.

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?

With no annotations, the description carries the burden: it discloses the operation is free and that it precedes paid queries, which tells the agent about cost and sequencing. It stops short of explicitly saying it is read-only or what shape the result takes, but for a zero-param lookup the risk surface is small.

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 with no filler. The core action comes first, followed by the conditional workflow hint, so it reads 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?

An output schema exists, so return values need no explanation, and with zero parameters the description covers everything needed to invoke the tool. It could add one clause on what to do with the returned names, but the essentials are present.

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 no parameters, so there is nothing to disambiguate; baseline 4 applies. Schema description coverage is 100% and the empty property set is consistent with the 'list all' framing.

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: listing all queryable GPU card models, and flags that it costs nothing. It does not explicitly name the sibling it differs from (gpu_price, gpu_history), but 'free' vs 'paid query' implicitly separates it from the paid lookups.

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 a clear workflow rule: call this before a paid query to obtain exact card names. This covers the when-to-use case well, though it doesn't state when-not-to-use or name the specific paid sibling tools it should precede.

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?

With no annotations, the description covers more than the baseline: it discloses the 30-day window, the sort key, the returned fields (current price plus 30-day low), and, importantly, per-call billing of $0.05 USDC, which is essential context for an agent deciding whether to invoke the tool. It does not mention rate limits, but for a read-only ranking tool this is fairly complete.

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?

It front-loads the purpose, then gives one concrete example and the billing line. Every element is useful, and the example row output is especially helpful, though the sentence is dense.

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?

There is no output schema and no annotations, yet the description still states what is returned (card model, current price, 30-day low, change percentage) and the cost. For a single-parameter read tool, an agent has enough information to invoke it correctly.

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 0%, and the only parameter, `top`, has no description in the schema. The example `market_movers(top=3)` makes clear that `top` controls how many entries are returned, which resolves most of the ambiguity; the default of 5 is already in 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?

The description gives a specific verb and resource: rankings of GPU price changes over the last 30 days, sorted by interval change percentage, with both drops and rises. It is clearly distinct from gpu_price and gpu_history, although it does not explicitly state 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?

The example implies the tool is used to get the top-N movers, but the description never says when to choose it over gpu_price (a current price) or gpu_history (a single model's trend). It provides no explicit exclusions or routing 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?

No annotations are provided, so the description carries the full burden, and it does disclose two useful behavioral facts: the call has no cost and it exposes a payment address, which matters for an agent in a paid marketplace. It says nothing about whether the call has side effects, whether it requires authentication, or about rate limits or latency, which leaves gaps for a tool with zero annotation coverage.

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 short sentences, front-loaded with the operation and its cost profile. The second half enumerates return fields that the output schema already covers, which is mild redundancy rather than waste, since it still helps an agent pick the tool.

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 output schema exists, so return values do not need spelling out, and the description still adds the two things the schema cannot: that the call is free and that it surfaces a payment address. What is missing is guidance on when in a workflow to call it, which for a health-check tool would be genuinely useful.

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 no parameter semantics to explain and the baseline of 4 applies. The description correctly does not invent parameter discussion.

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 identifies a specific operation ("健康检查") and enumerates what it returns (service status, payment address, network, price list). It is unmistakably different from every sibling (gpu_history, gpu_price, list_gpu_models, market_movers, platform_reliability), though it never explicitly contrasts itself with any of 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?

Saying the call is "免费" (free) implies it is a cheap pre-flight/connectivity check, and the way it is framed suggests calling it before spending money on the paid data tools. However, the description never states when to use this tool rather than a sibling, and it gives no prerequisites or exclusions.

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?

With no annotations provided, the description carries the full disclosure burden. It does add real value beyond structured fields: it states the return shape (26 platforms scored, grade plus component metrics) via a concrete example and discloses a per-call cost of $0.05 USDC. It does not state read-only nature explicitly (implied by zero params), nor mention caching, rate limits, or determinism.

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 purpose and the important interpretive caveat are front-loaded, followed by a concrete example and the cost. Every sentence carries information, though the example is somewhat dense and the description could be split more cleanly between what it measures and what it returns.

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, so the description must convey return values, which the worked example does (26 platforms, grade, and component breakdown). Given a zero-parameter read tool, an agent has enough to call it correctly; only minor operational details like freshness or caching are absent.

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?

There are zero parameters, so the baseline of 4 applies. The example call platform_reliability() reinforces that no arguments are accepted, leaving no ambiguity about invocation.

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 the exact resource (GPU rental platforms) and the specific metric computed (data-monitoring coverage score A~E with four named sub-metrics: scrape success rate, days included, inventory sufficiency, model coverage). The explicit caveat that this measures the data source's coverage and not the platform's own service quality cleanly separates it from price-oriented siblings like gpu_price and gpu_history.

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 implied rather than stated: an agent can infer this is for gauging how trustworthy this data source's coverage of a given platform is, but the description never says when to call it versus a sibling, nor what to do with the result. No explicit exclusions or prerequisites are given.

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?

With no annotations, the description carries the full burden and does well: it discloses that the recommendation includes cheapest platform and price, estimates total cost using an 8-hour assumption, and charges $0.05 USDC per call. It does not cover data freshness, failure behavior for unrecognized tasks, or availability constraints, but the core behavioral traits are disclosed.

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 purpose is front-loaded and the description is generally efficient, with task examples, coverage, return fields, and billing covered in a compact block. The repeated example framing and marketing sentence add slight redundancy but do not obscure the core 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?

For a recommendation tool with no output schema and no annotations, the description supplies the key return fields, billing model, task coverage, and input examples. It is nearly complete for an agent to call correctly, with minor gaps around response structure details and handling of unsupported tasks.

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 0%, so the description must compensate for the single 'task' parameter, and it does: it gives Chinese and English examples, lists eight task categories, and shows a sample call. It still leaves the exact accepted vocabulary open-ended rather than defining validation or fallback behavior.

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 and resource: recommend the cheapest GPU rental plan for a given task, returning card type, platform, hourly price, and estimated total cost. It also distinguishes itself from sibling pricing tools by framing the capability as task-based quoting ('按任务问价'), which is not what gpu_price or gpu_history do.

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?

It gives clear usage context with example task inputs in both Chinese and English and lists eight supported task categories, making it easy to know when this tool applies. It does not explicitly name when to use gpu_price instead or when not to use this tool, so it stops short of full alternative routing.

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