SpareHive GPU Advisor

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 the returned fields and the concrete 60-day staleness threshold, which is real behavioral context beyond the (empty) schema. It does not explicitly state that the operation is read-only, but a zero-parameter reporting tool has no mutation surface to warn about.

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, zero waste: the reporting content is front-loaded and the invocation trigger follows. Nothing is repeated or 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 and no annotations, the description still tells the agent what it will receive (per-file update time, days since, threshold flag), which is enough to decide and invoke. Minor gap: no indication of which data files are covered or the return format.

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 schema takes no parameters, so per the rubric the baseline is 4. There is nothing parameter-related the description needs to compensate for.

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 ('报告数据的新鲜度') and enumerates exactly what is reported: each file's update time, days elapsed, and the 60-day threshold flag. The purpose is unambiguous and clearly distinct from the model/hardware-focused siblings, though it does not name a sibling 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?

Gives an explicit trigger: '当用户质疑数据是否过时时调用' (call when the user questions whether the data is stale). That is a clear when-to-use condition, but there is no when-not guidance and no named alternative tool.

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 does disclose a real behavioral contract — the outcome vocabulary (fits/tight/no/unknown), remaining VRAM, memory breakdown, remediation suggestions, and the card's rental price — which is genuinely useful. It says nothing about cost, rate limits, error behavior for unknown GPUs, or whether the call is read-only, leaving meaningful gaps.

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 dense sentence that front-loads the verdict and then enumerates the returned payload. Nothing is wasted, though the return-value inventory could have been trimmed or left to an output schema.

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 usefully spells out what comes back and what the 'no' case offers. It is nearly sufficient for invocation; the main omissions are the meaning of 'unknown' (bad model/GPU id vs. genuinely unknown) and any note on the cost of the check.

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 50%: gpu (id or alias) and context_tokens (default 4096) are documented, while model and quant carry no schema description. The description confirms quant and context are inputs that change the verdict but adds no format guidance (e.g. accepted quant names, context limits), so it only marginally compensates.

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 precise verb+resource+conditions: judging whether a model at a given quantization and context length fits on a specific GPU. That is clearly distinct from search_models or data_status, though it never names the siblings (gpu_prices also covers rent, model_requirements also covers sizing) so the boundary is inferable rather than explicit.

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 by the phrasing 'specify quantization, specify context', which tells the agent these knobs affect the answer, but there is no explicit when-to-use statement, no prerequisite (e.g. the GPU/model must be resolvable), and no pointer to model_requirements or gpu_prices for the underlying data.

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 supplied, so the description carries the disclosure burden. It does well on data-coverage transparency (only RTX 4090 priced, others 未定) and lists the returned metrics, but it never states that the operation is read-only/no-side-effect, nor any auth or rate constraints — an important omission given 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 tight sentences, front-loaded with the purpose and followed by the enumerated metrics and the coverage caveat. No filler; every clause carries information an agent needs.

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 must carry the return-value burden, and it does by naming each metric the response contains. The main remaining gap is behavior when the optional `gpu` param is omitted and the lack of a read-only guarantee, but for a simple two-param lookup this is close to 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 50%: `gpu` has no schema description at all and `include_competitors` only says '默认 true'. The description partially compensates by implying `gpu` selects the GPU type and that 竞品中位价 is a returned field tied to include_competitors, but it never gives valid GPU-type values or explains what omitting `gpu` (0 required params) does.

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 (查询) and resource (GPU 租价), then enumerates exactly which price metrics are returned (即时档/排队档报价, host 实得, 电费成本, 竞品中位价, 国内零售价). This is distinguishable from siblings like model_requirements or fit_check, which deal with capability/fit rather than pricing.

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 by the verb+resource — an agent can infer this is the tool for price lookups — and the scope caveat (只有 RTX 4090 有报价) usefully warns of sparse data. However, there is no explicit when-to-use/when-not guidance and no mention of the sibling tools (data_status, search_models) that might be the right first stop.

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 behavioral burden. It does disclose a genuinely useful trait — data may be stale, and the response surfaces data_updated and stale — but says nothing about permissions, rate limits, or whether the fit verdicts are cached.

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 dense sentence with the verb front-loaded and zero filler. It reads as a run-on list of return fields, but every clause carries information and nothing is wasted.

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 does the heavy lifting by enumerating the return fields (VRAM figures, evidence grade, fit verdicts, rental price) and the staleness signal. Only the relationship to the fit_check sibling is left unexplained.

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 the schema already documents all three parameters, giving a baseline of 3. The description adds only marginal meaning, clarifying that quant drives the reported weight size, and does not touch context_tokens at all.

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 (查询) and resource (部署某模型需要的硬件配置) and enumerates what is returned: weight size, min/recommended VRAM, evidence level/source, per-GPU fit verdicts and rental price. This is clearly distinct from gpu_prices and search_models, though it overlaps with fit_check on the fits/tight/no verdict without explicitly differentiating itself.

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 gives no when-to-use guidance, no prerequisites, and never names an alternative such as fit_check for a single combo or gpu_prices for pricing. Usage is only implied by the tool's purpose, leaving the agent to infer routing among four siblings.

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, so the description carries the burden, and it does disclose the return fields (id, type, minimum VRAM, parameter scale) plus the alias-matching quirk. It does not state that the operation is read-only or how results are capped, leaving some gaps.

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?

Three short sentences with no filler; the core action and the aliasing caveat are front-loaded before the return-value summary.

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 three-parameter read tool with no output schema, listing the returned fields makes it actionable. Missing only minor details such as the interaction between limit and the full listing 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 67%, and the description's query explanation largely repeats the schema's own text. The alias example (Qwen3.8 27B / qwen3.8-27b) adds real value, but type and limit receive no treatment in the description.

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?

Specific verb plus resource ('按名字或类型查找模型 ID') with an example of the alias matching behavior. It clearly separates itself from the sibling model_requirements by framing this as the ID-confirmation step.

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 tells the agent to call this before checking VRAM requirements, and states that omitting the query lists all models. It routes to the right workflow but doesn't explicitly name the alternative tool or cover when to filter by type instead.

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