compute.pangle.online — GPU rental market

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

With no annotations available, the description carries the burden of explaining behavior. It usefully discloses the fee calculation details, the minimum and cap amounts, per-lease-per-day charging, and pricing at event-time rate. However, it never explicitly states whether this is a read-only retrieval, what operations are performed, or what the response shape looks like.

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 compact and front-loaded, starting with the core purpose and then providing the fee detail and the useful distinction from fee_bps. No words are wasted, though the long parenthetical fee explanation makes the first sentence somewhat 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?

The description gives a solid overview of what the ledger reports and how fees are computed, which is useful for a two-parameter read-style tool. However, it omits parameter semantics, an explicit when-to-use statement, and any indication of response structure or operational limits, leaving clear gaps given the absence of annotations and an output schema.

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%, and the description says nothing about account_token or days. The agent receives no additional meaning about how the usage ledger is scoped, how far back the data reaches, or the role of the required account_token. The mention of 'per UTC day' is only weakly related to the days parameter and does not explain it.

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 clearly identifies account_usage as a metered usage ledger and enumerates its contents: placements, destroys, estimated hours, USD, and platform fee. It names the tool's focus unambiguously, though it does not explicitly differentiate itself from a sibling tool by name or contrast with an alternative.

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 no guidance on when to use account_usage versus a sibling tool such as true_cost_notes or idle_history. The only distinction is a contrast with fee_bps, which appears to be a field or separate concept, not a tool alternative, so the agent is left without usage conditions 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, the description carries the behavioral burden. It discloses that throughput is based on dense vendor spec sheets and that models without defensible specs are marked unrated. It does not mention output shape, pagination, or data freshness, but the read-only, board-like behavior is reasonably clear.

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 three compact sentences with no filler. The core metric is front-loaded, the parenthetical adds necessary precision, and every sentence contributes either definition or usage guidance.

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 ranking tool with no output schema and no annotations, it explains the core computation and unrated behavior but omits parameter effects and return format details. It is adequate for selecting the tool but not fully complete for invoking it with confidence.

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% and the description does not explain any of the three parameters. 'limit' and 'min_vram_gb' are fairly self-explanatory, but 'offer_class' remains ambiguous, and the description adds no meaning to the parameters beyond their names.

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 clearly defines the tool as a per-GPU-model ranking board that computes TFLOPS per dollar-hour from live price and FP16 throughput, and distinguishes it from a 'cheapest card' query. It lacks a formal verb like 'list' or 'return', but the resource and metric are specific enough to be identifiable.

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 explains that this tool answers 'most compute for my budget' rather than 'cheapest card', which gives clear selection context. It does not explicitly name sibling tools or state when to prefer them, but the contrast is enough to guide an agent.

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 burden of disclosing behavioral traits. It notes the prerequisite for order_secret but does not describe the consequences of cancellation (e.g., destructive, irreversible) or any side effects. For a cancel operation, this is insufficient.

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 very concise—two short sentences with no fluff. It front-loads the primary action and adds a necessary prerequisite. However, the brevity may contribute to under-specification, but that is a matter of other dimensions. For structure, it is efficient and well-ordered.

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 cancellation tool with no annotations and no output schema, the description is incomplete. It does not explain when cancellation is appropriate, what happens to the order after cancellation, any authentication or authorization requirements beyond the secret, or error scenarios. An agent would struggle to use 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?

The schema has no descriptions for order_id or order_secret, and the description only adds meaning for order_secret by noting it comes from create_limit_order. order_id remains undefined. Since schema coverage is 0%, the description does not fully compensate, leaving both parameters under-documented.

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 clearly states the action: 'Cancel a limit order.' This is a specific verb and resource. However, it does not differentiate this tool from siblings such as fill_limit_order or limit_order_status, which could also relate to limit orders. The clarity is good but not complete.

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 no guidance on when to use this tool versus alternatives. It only mentions that order_secret is required from create_limit_order, but does not outline conditions for cancellation or when not to use it. This is a significant gap.

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 exceeds it: it discloses key-handling, one-time secret exposure, ticket lifetime, poll cadence, 30-day expiry, re-arm semantics, watchdog webhooks, and hard-stop budget behavior. This is exceptional transparency for a financial mutation tool.

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 long but justified by the tool's complexity. It front-loads the core mechanism, then expands into standing behavior and watchdog limits. A few phrases are somewhat verbose, but every major paragraph adds essential operational knowledge.

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?

Despite having no output schema and no annotations, the description tells an agent nearly everything needed to call this tool correctly: execution model, security boundaries, timing behaviors, failure modes, and limits. It even explains what the response notably contains (order_secret shown once).

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. It explains the core parameters well: max_price_per_gpu_hr, standing, max_refills, cooloff_seconds, spend_alert_usd, age_alert_hours, auto_destroy_budget_usd, and offer_class. However, min_vram_gb, min_gpu_count, and webhook_url are not explicitly mapped to their schema fields.

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 opens with a specific verb and resource: 'Place a standing LIMIT ORDER' with a precise price condition. It distinguishes itself from sibling order tools by detailing the fill-ticket mechanism and sidecar execution path rather than just saying 'create an order.'

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 strong context about when standing mode is appropriate and how re-arming works, but it never explicitly contrasts this tool with immediate-rental siblings like rent_best or renter_offer, nor states when NOT to use it. Usage is implied clearly but alternatives are not 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?

With no annotations provided, the description carries the full burden. It discloses the trigger condition, the 5-minute polling interval, the re-arm behavior, the returned id, and webhook semantics. It also hints at the fee-adjusted pricing. Nothing is hidden, and there's no contradiction.

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 dense but not wasteful. It front-loads the core trigger and then adds re-arm and webhook details. It reads as a single paragraph but logically flows from purpose to operational details. Slight room for bullet-point structuring, but it's appropriately sized.

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 create operation with no output schema and no annotations, the description covers the return value (watch with id), polling instructions, webhook behavior, and re-arm logic. It doesn't mention error cases or rate limits, but these are not essential for a basic create call. It's complete enough for an agent to use 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%, so the description must compensate. It explains gpu, offer_class (optional), max_price_per_gpu_hr, and webhook_url semantics through context, even though it doesn't run through each parameter explicitly. It adds meaning beyond the schema by describing the matching logic and the role of each parameter in the tripwire.

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 (create) and resource (price watch/tripwire) with a precise trigger condition. It clearly distinguishes from siblings like watch_status (which polls) and delete_watch (which removes). The behavior is unambiguous and self-contained.

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 explains how the watch works (fires when price drops, re-arms on 2% climb) and mentions polling and webhook options. It implies when to use it (to monitor price thresholds) but doesn't explicitly contrast with alternatives like create_limit_order. Still, the context is clear enough for an agent to select it.

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 burden of behavioral disclosure. It states 'Delete', which implies mutation, but does not mention that the action is likely irreversible, whether confirmation is required, or how errors are handled. This lack of detail is a significant gap for a destructive operation.

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, concise sentence with no redundant words. It front-loads the action verb and is appropriately sized for a tool with one parameter and no output schema. Every word earns its place.

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 delete operation, the description is minimal but not fully complete. It does not mention return values, error behavior, or postconditions. Given the tool's low complexity, this is acceptable but leaves gaps an agent might need to handle, especially regarding destructiveness and failure modes.

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 description coverage is 0%, so the description must compensate by clarifying the parameter. It merely says 'by id', which restates the watch_id parameter name from the schema without adding format, source, or validation details. The singleton parameter is self-explanatory in name, but the description provides no added meaning.

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 clearly states the action (Delete), the resource (watch), and the identifier (by id). It is specific and unambiguous, distinguishing itself from siblings like create_watch or watch_status. However, it does not elaborate on what a 'watch' is or whether the deletion is permanent, which could add clarity but is not essential for the basic purpose.

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 no guidance on when to use this tool versus alternatives such as cancel_limit_order or destroy_rental. It does not mention prerequisites, permissions, or conditions under which deletion is appropriate. Usage is only implied by the verb 'Delete', leaving an agent to infer the decision logic.

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 behavioral burden. It clearly discloses the mutating nature (destroy) and adds a useful security trait ('key passes through and is never stored'), but it does not state irreversibility, billing effects, or failure behavior.

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 with no filler; the action is front-loaded and the security note is a single additional clause.

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 destructive action, it covers the action, target, provider options, and key-handling behavior. It omits response/confirmation details, but no output schema exists and an agent can still invoke the tool 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?

Most parameter meaning is left to field names; however, the description adds the allowed provider scope (vast or runpod) and clarifies that api_key is passed through rather than stored. It does not explain how to obtain provider_instance_id, so compensation is partial.

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 clear destructive action ('Destroy') on a specific resource ('an instance previously placed on your account'), and scopes it to vast or runpod. This separates it from sibling tools like rental_status or rent_best.

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 phrase 'previously placed on your account' establishes the precondition and target context. It does not explicitly name alternatives or exclusions, so an agent must infer that other rental-related tools are for status or creation.

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 delivers. It discloses that the provider key is used once and never stored, that the call is idempotent per order, that the offer is re-quoted live, and that the budget guard arms exactly like rent_offer. This is genuinely useful non-obvious behavior.

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?

Four dense sentences, with the main action and key constraint front-loaded in the first sentence. Some jargon like 'ticketed' and 'line' could be clearer, but every sentence adds distinct value and there is 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?

The core execution mechanics, idempotency, and budget behavior are well covered. However, with no annotations and no output schema, an agent still lacks guidance on the default parameters, what the 'receipt' return value contains, and how to interpret the other five schema fields. For a financial mutation with 8 parameters, this is not fully complete.

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 needed to document 8 parameters. It effectively explains api_key ('provider key... never stored'), order_id/order_secret ('per order', 'order's line'), and mentions auto_destroy_budget_usd, but that field is not even in the schema. Params like image, disk_gb, confirm, dry_run, and account_token are left completely unexplained.

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 ('execute') and resource ('limit order's live fill ticket'), and immediately distinguishes this from order lifecycle siblings like create_limit_order, cancel_limit_order, and limit_order_status. The 'Only works while the order is ticketed' condition gives a crisp scoping boundary.

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 clearly says when the tool is valid ('Only works while the order is ticketed') and describes a failure condition ('refused above the order's line'). It references rent_offer for budget-guard semantics, but it does not explicitly name alternatives or say when not to use this tool in favor of another.

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 exist, so the description carries the behavioral disclosure burden. 'Fetch' implies a read-only retrieval, but the description does not state what happens for missing ids, permissions, rate limits, or any side effects. This is acceptable for a simple getter but not richly transparent.

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 sentence, no filler, and the core scoping detail is front-loaded. Every word contributes.

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 one-parameter getter, the description covers the tool's purpose and id provenance. It could add not-found or response-shape details since no output schema exists, but the tool is simple enough that these are not critical gaps.

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 only exposes a string 'offer_id' with no description; the description adds meaningful context by saying the id comes from search_offers. This tells an agent where to obtain a valid offer_id, which is important with 0% schema coverage.

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 uses a specific verb 'Fetch' with a clear resource, 'one offer by its id', and differentiates itself from search_offers by indicating it retrieves a single existing offer rather than searching. Sibling ambiguity is minimal.

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 parenthetical '(from search_offers)' gives useful context: the agent should first run search_offers to obtain the id, then call get_offer. It does not explicitly state when not to use it, but the by-id nature makes the usage context clear.

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?

The description states the output fields (ts, idle_share, idle_offers, total_offers) but does not explicitly confirm a read-only operation, mention side effects, or address authentication/rate limits. The given output format partially compensates for missing annotations.

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 succinct, using two sentences to convey the purpose and output format without unnecessary detail. It is well-structured and easy to parse.

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 description provides the output field names but lacks context on units, data interpretation, error handling, or response format. It also does not explain the meaning of each field beyond idle_share, leaving the tool's full behavior underspecified.

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 only parameter, 'hours', has no description beyond its name and default value. The description does not explain what 'hours' represents, how it relates to the output points, or the valid range. Schema coverage is 0%.

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 clearly identifies the tool as providing an idle-capacity index over time, with explicit mention of the metric (share of interruptible GPU capacity) and the output fields. It distinguishes this from other tools by focusing on idle capacity 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?

No guidance is given on when to use this tool versus alternatives, such as other history or status tools. The description does not mention any preconditions or specific scenarios where this is preferred.

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 of behavioral disclosure. It discloses the return contents (status, last price, recent events) and the conditional behavior triggered by order_secret. It does not mention whether this is a read-only operation or any side effects, but for a status tool the lack of explicit read-only messaging is a moderate gap, not a contradiction.

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 concise sentences with no filler. The main return values are front-loaded, and the conditional secret behavior is clearly separated. Every clause contributes useful 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?

The description covers the core outputs and the conditional behavior, which is reasonable for a simple status tool. However, with no output schema and no usage guidance, an agent still needs to infer when to call this over related order tools and what exact response format to expect. It is adequate but not fully complete.

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. It adds meaning to order_secret by explaining that it unlocks the live fill ticket. However, order_id is not described beyond its name, and the overall parameter guidance is minimal for a tool with zero schema descriptions.

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 clearly identifies the resource (limit order) and the kind of data returned (state, last price, events). It lists concrete state values, which makes the purpose tangible. However, it is a noun phrase rather than an explicit verb+resource statement, and it does not differentiate from sibling status tools beyond the resource name.

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 no explicit guidance on when to use this tool versus alternatives such as cancel_limit_order or fill_limit_order. It only implies the conditional use of order_secret for reading the live fill ticket. There are no when-to-use or when-not-to-use signals.

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 behavioral disclosure burden. It communicates that the data is live and enumerates the output dimensions, and the word 'snapshot' implies a read-only operation. It does not define how 'freshness/health' is computed or specify the exact response structure, but it is reasonably transparent for a zero-parameter stats tool.

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 front-loaded sentence that moves from the core concept ('Live market snapshot') to a scannable colon-separated list of output fields. Every term earns its place and there is no filler or redundant wording.

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 zero-parameter, read-only market snapshot with no output schema, the description covers the essential return values an agent needs to decide whether to call it and how to use the result. It could be slightly more explicit about the return format or units, but nothing critical is missing for selecting and invoking the 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?

The tool has zero parameters, so the input schema is already complete and no parameter documentation is needed. The description does not add parameter-level meaning, but that is unnecessary here; the baseline of 4 applies because there is nothing for the description 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?

The description clearly identifies the tool as a live market snapshot and enumerates the specific metrics it provides (total offers, GPU models, idle-capacity share, per-provider freshness/health, cheapest price per model, fee_bps). This distinguishes it from sibling tools like get_offer or search_offers, though it lacks an explicit action verb and does not name any sibling directly.

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 phrase 'live market snapshot' implies this is the right tool for a high-level overview of current market conditions rather than for specific offer searches or historical analysis. However, there is no explicit guidance about when to use this tool instead of related siblings such as best_value, spot_spread, or provider_reliability.

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 burden and does a good job: it discloses the output format ([ts, min_price_per_gpu_hr, median_price_per_gpu_hr, offer_count]), the bucketing behavior for different time ranges, and accepted offer_class values. It doesn't discuss error cases or auth/rate limits, but for a read-only historical query this is reasonably transparent.

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 compact, front-loaded with the core purpose, and every sentence adds useful information: naming requirement, offer_class constraint, output schema, and bucketing behavior. There is no filler or repetition.

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?

While the description richly explains output shape and bucketing, it leaves key invocation details unresolved: what gpu means, how gpu_model differs, what hours represents, and which parameters are actually expected. Without annotations or output schema and with four undocumented parameters, an agent cannot reliably construct a correct call.

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. It clarifies offer_class values and gives an example for GPU model naming, but it does not explain the meaning of the gpu parameter, the gpu_model parameter, or the hours parameter. The ambiguity between gpu and gpu_model is especially problematic because both exist as separate fields with empty defaults.

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 clearly states that this tool returns price history for a single GPU model, with a concrete example ('RTX 4090') and specifies the output is a per-provider time series. It is distinguishable from siblings like market_stats or idle_history, though it does not explicitly name or contrast those alternatives.

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 when to use the tool by requiring an exact GPU model name from search_offers/market_stats and by constraining offer_class to on_demand or interruptible. However, it does not explicitly state when not to use it or which sibling tool should be chosen for other kinds of price/market queries.

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 of behavioral disclosure. It clearly states that the tool is descriptive rather than a forecast, which is a critical trait for an agent to know it won't get predictions. It also lists the outputs (percentile, 24h trend, verdict), giving a clear picture of the result. It does not mention any limitations or additional caveats, but for a descriptive tool this is sufficient.

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, densely informative sentence. It front-loads the context ('Rent-now-or-wait context'), then describes the output and nature. Every word earns its place; there is no fluff. It is appropriately concise for a simple 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 description adequately conveys what the tool returns (percentile, trend, verdict) and its descriptive nature, so an agent knows what to expect. However, the lack of parameter guidance means the tool cannot be called correctly without additional inference, especially the distinction between gpu and gpu_model and the meaning of offer_class. Given the missing output schema and 0% parameter coverage, the description does not fully equip the agent.

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 covers 0% of parameters (no descriptions), and the description does not compensate by explaining gpu, gpu_model, or offer_class. The phrase 'one GPU model' hints at gpu_model, but gpu and offer_class are completely unexplained, including their distinction or valid values. This is a significant gap, making it hard for an agent to know how to populate the parameters correctly.

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 clearly states the tool's purpose: it situates the current best price for a GPU model within its trailing 7/30-day range, providing a percentile, 24h trend, and verdict. It explicitly says it is descriptive, not a forecast, which distinguishes it from predictive or raw historical tools like price_history. This is specific and unambiguous.

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 opens with 'Rent-now-or-wait context', which gives a clear indication of when to use it: for deciding whether to rent now or wait for a better price. It does not explicitly name alternatives or state conditions for not using it, but the context is strong enough for an agent to infer its scenario. Lack of explicit sibling routing keeps it from a 5.

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 of behavioral disclosure. It clarifies that feed_score is about data, not hardware, and describes the trailing window and the optional machine reliability field. This gives a good sense of what the tool returns and how to interpret it, though it doesn't explicitly state that it's a read-only operation.

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 concise, front-loads the main purpose, and adds a clarifying note about feed_score without unnecessary detail. It's well-structured and readable.

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 tool with one parameter and no output schema, the description covers the return contents and interpretation. It's complete enough for an agent to call the tool correctly, though it could explicitly link days to the trailing window and mention the output 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 description mentions a 'trailing window' which implicitly maps to the 'days' parameter, but it doesn't explicitly state that 'days' controls the window length. With only one simple integer parameter and schema coverage of 0%, the description partially compensates but leaves the connection implicit.

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 clearly states what the tool does: returns a measure of each provider's data feed dependability (poll success over a trailing window) and optional machine reliability. It distinguishes itself from siblings by focusing on reliability, not market or account data, though it doesn't explicitly name an alternative.

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 explains what the tool measures but provides no guidance on when to use it versus other tools, nor any exclusions or prerequisites. An agent would have to infer usage from the description alone, which is insufficient.

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. It discloses the one-time token display, the hash storage, and the ledger persistence. This adds important behavioral context beyond the schema, though it omits details like idempotency or error conditions, which are less critical for a registration tool.

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 two sentences with no unnecessary words. The primary action is front-loaded, and secondary details follow logically. It's concise and well-structured, though slightly run-on in the first sentence.

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 tool with a single optional parameter and no output schema, the description explains the return value, how to use it, and optionality. It doesn't cover failure modes or side effects beyond persistence, but the core usage is adequately covered.

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 provides 0% coverage (no parameter descriptions) and the tool description never mentions the 'label' parameter. An agent has no indication of what label does, whether it's meaningful, or if it affects account creation. This is a critical gap.

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 action 'Create a metering account' with a specific resource and explains what it returns (bearer token) and how to use it. It also hints at the read-back mechanism via account_usage, making it distinct from other tools without needing to name 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?

Provides explicit context: the tool is optional ('Optional — renting works without one') and directs how to use the token on rent calls. It doesn't explicitly list alternatives, but the tool is unique in generating accounts, so the guidance is sufficient.

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 full behavioral disclosure. It explicitly declares the operation read-only and explains that the API key passes through and is never stored, which is important for safe invocation. It does not discuss rate limits or error behavior, but the safety-critical traits are covered.

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 compact and front-loaded with the core purpose, then adds the read-only and key-handling safety information. Every clause earns its place, with no redundant or filler 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 three-string-parameter status tool with no output schema, the description gives enough return-value context by listing statuses, uptime, price, spend, and budget guard state. It does not mention not-found or error behavior, but the essential calling context is 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?

Schema description coverage is 0%, and the description does not compensate by explaining provider, provider_instance_id, or api_key. It only references 'your key' and 'an instance on your account,' leaving the provider parameter and instance identifier semantics largely implicit despite the parameter names being somewhat self-explanatory.

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 clearly states what the tool does: it reports the live state of an instance on the user's account, listing the specific statuses and data fields included. This distinguishes it from sibling tools like destroy_rental, limit_order_status, and watch_status without requiring the agent to inspect schemas.

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 'live state of an instance on your account' and 'Read-only,' which signals when this tool is appropriate. However, it never names alternatives or states when not to use it, leaving the agent to infer the distinction from sibling names rather than explicit 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. It discloses dry-run default, live re-quote, absolute price cap, bounded retry behavior (at most 3), and reporting of skipped offers. This is meaningful behavioral context beyond a simple 'rent cheapest' statement.

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 compact and front-loaded with the core action, then adds essential behavioral guarantees and fallback logic. No sentence is wasted, though the dense parenthetical list and example could be slightly clearer.

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 16-parameter tool with no annotations and no output schema, the description covers the key behavioral contract (dry-run, re-quote, price cap, walk-to-next) but leaves many filter parameters unexplained and does not describe the response shape beyond 'reports what it skipped'. It is adequate but incomplete for full autonomous invocation.

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%, and the description only explains max_price_per_gpu_hr (absolute) and dry_run (default true). The other 14 parameters, including gpu, image, provider, min_vram_gb, confirm, and idempotency_key, are not given any semantic explanation beyond their raw titles, so the description does not compensate for the schema gap.

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: it searches the live book and rents the best (cheapest) matching offer. It distinguishes itself from siblings like rent_offer and search_offers by framing itself as a one-shot best-offer action, so an agent can tell what it does.

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 conveys when to use it via the example intent ('cheapest H100 under $2/hr') and references rent_offer's contract for guarantees. It does not name alternative tools for exclusion, but the 'one-shot' phrasing and 'walks to the next' behavior imply a fast auto-select path versus manually searching then renting.

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 transparency burden and excels: it discloses that the API key is never stored, that the offer is re-quoted live and refused above max price, and that auto_destroy_budget_usd holds the key in process memory only and destroys the rental at the cap, with a station-restart caveat. This is exceptionally candid about side effects and safety behavior.

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 dense but every sentence earns its place: key handling, dry-run default, live-placement conditions, price guard, idempotency, budget destruction behavior, and account attribution. It front-loads the most critical safety facts and has 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?

For a high-stakes side-effecting tool with 11 parameters and no output schema, the description covers nearly all behavior an agent needs: dry-run semantics, idempotency, budget guard, and key safety. Minor gaps remain around the return/receipt shape and the optional image/label/disk_gb parameters, but the essential call semantics are well specified.

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 add meaning beyond parameter names. It does this well for api_key, dry_run, confirm, max_price_per_gpu_hr, idempotency_key, auto_destroy_budget_usd, and account_token. However, image, label, disk_gb, and offer_id are left to their names and schema defaults, so coverage is strong but not complete.

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 opens with a specific verb and resource: 'Place a rental on YOUR provider account,' and it names the target offer types ('vast and runpod offers'). This clearly distinguishes rent_offer from sibling tools like rent_best or search_offers by focusing on executing a specific, already-chosen offer rather than selecting one.

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 clear operational guidance: dry run is the default, live placement requires both dry_run=false and confirm=true, and retries should use an idempotency_key. It also references register_account for account_token and rental_status as a backstop, though it does not explicitly state when to choose this tool over rent_best or rent_x402.

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 full responsibility for disclosing behavior, and it does so thoroughly. It reveals the two-call payment flow, that refunds do not exist, that the payer is charged only after the lease is live, that ssh_pubkey is written into the container's authorized_keys and nowhere else, and that originator never affects price or settlement. It also discloses MCP-specific transport differences without misleading the caller.

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 long but densely packed with necessary operational detail. It is well-structured with paragraphs for each key parameter and a clear MCP note at the end. No sentence is purely filler, though some redundancy around 'same value' and 'byte-identical' could be tightened. The front-loading of the core concept helps an agent quickly grasp the tool's 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?

For a complex paid tool with no output schema and no annotations, the description covers the payment challenge, required signing flow, ssh access, lease_id provenance, originator semantics, receipt publication, and a frozen wire-contract doc link. The main gap is incomplete documentation of the blocks parameter and no explicit error-handling or failure-mode guidance. Even so, the description is substantially complete for an agent to invoke the tool 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 description coverage is 0%, so the description must compensate for all parameters. It does: x_payment, ssh_pubkey, lease_id, and originator are each explained with format, purpose, and constraints. However, the blocks parameter is only implied via 'prepaid block' and the default behavior of calling with no arguments; its semantics, bounds, and effect on price are not explicitly stated. This leaves one parameter underdocumented.

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 opens with a specific verb-resource pair: 'Rent a prepaid block of GPU time' and clearly identifies the unique payment mechanism (x402 HTTP 402 micropayments, USDC on Base). It distinguishes this tool from its siblings by emphasizing no account, no API key, and no human, which no other rental tool appears to state. The two-phase payment challenge explanation further removes ambiguity.

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 explicit, step-by-step usage: call with no arguments to get the challenge, sign the EIP-3009 authorization, then call again with x_payment. It also states when each parameter is required and the role of lease_id. It does not explicitly compare against alternatives like rent_offer or rent_best, but the usage context is clear enough that an agent can determine when to invoke it.

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?

Since no annotations are provided, the description carries the full burden of behavioral disclosure. It explicitly states that results are ranked cheapest-first by price_per_gpu_hr and details filter semantics (e.g., country accepts ISO-2, comma lists, or 'EU'). It does not disclose potential side effects (likely none for a search), authentication requirements, or rate limits, but the core behavior is transparently described.

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 compact set of sentences that front-loads the primary action and immediately explains key filter semantics. Every sentence earns its place—there is no fluff or repetition. The structure is easy to scan and digest.

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 the complexity (10 optional parameters, no annotations) but the presence of an output schema, the description covers the main filter behaviors and the result ordering. It does not mention pagination or error handling, but the limit parameter is present in the schema and the return format is presumably covered by the output schema. Overall, it provides sufficient detail for correct invocation.

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. It adds meaning to five parameters (gpu, offer_class, country, min_gpu_count, region) with concrete examples and constraints, but leaves others (limit, provider, min_vram_gb, min_reliability, max_price_per_gpu_hr) to their self-explanatory names. It adds significant value for the most nuanced parameters but does not fully cover all ten.

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 opens with a specific verb-resource pair, 'Search live GPU offers,' and clarifies the domain with concrete GPU model examples ('4090', 'H100'). It differentiates from siblings by focusing on the search/listing behavior rather than retrieval or rental actions, making its purpose instantly clear.

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?

While the description implicitly positions this as the tool for exploring available offers, it provides no explicit guidance on when to choose it over alternatives like get_offer or best_value. It lacks direct statements such as 'use this to browse offers before renting' or 'for a specific offer, use get_offer'. The usage context is implied but not spelled out.

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 exist, so the description must disclose behavioral traits. It implies live data ('live discount') and conditional history, but does not state that it is read-only, whether it depends on authentication, or any rate limits or side effects. It lacks clarity on what happens without gpu_model (just the spread) and whether history is supplementary or exclusive. This is a significant gap given zero annotation support.

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, compact sentence that front-loads the core concept (on-demand vs interruptible spread) and appends the optional history note. It is appropriately sized for the tool's simplicity, with no redundant words. The structure is efficient and easy to parse.

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 and no annotations, the description leaves many gaps: what exactly is a 'spread'? How does the 'hours' parameter affect results? What does the tool return (discount percentage? rate per hour?)? The history behavior is only vaguely described. For an agent to call this correctly and interpret the response, crucial context is missing, making the description incomplete for its complexity class.

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. It explains that gpu_model triggers history, but does not explain the 'hours' parameter at all. The schema already shows defaults, but the description adds no meaning for hours (time window? granularity?). It partially covers gpu_model but leaves hours unexplained, which is insufficient for a 0-coverage tool.

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 concept: the live discount for idle capacity per model, contrasting on-demand vs interruptible pricing. It clearly identifies the resource (spot pricing spread) and the optional history behavior. It does not explicitly differentiate from sibling tools like idle_history or price_position, but the phrasing is sufficiently specific to avoid tautology.

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 offers a usage hint ('pass gpu_model for its history too') but gives no guidance on when to choose this tool over alternatives. It does not mention exclusions, typical use cases, or relationships with siblings. An agent has to infer that this tool is for discount information, but no comparison to similar tools is provided.

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 to carry the safety or data-provenance burden, the description fully discloses what the tool provides: provider-verified claims with source URL and date, specific billing dimensions, and the limitation that unverified providers carry no claims. This transparency about scope and provenance is strong.

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 dense but every clause earns its place: it states the subject, the verification standard, the specific coverage areas, and the unverified-provider caveat. It is front-loaded with the key billing-fine-print concept and contains 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?

For an information-only, zero-parameter tool with no output schema, the description fully specifies content, provenance, and limitations. An agent can decide to call it and know what to expect without needing further details.

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 for the description to explain. The 100% schema coverage and empty input schema confirm no arguments are needed, and the description adds no irrelevant parameter language.

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 clearly identifies the tool as a source of billing fine print and true-cost caveats behind provider hourly rates, and it is distinct from sibling price/market tools. It lacks an explicit verb such as 'returns' or 'lists', but the content and scope are unmistakable.

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 conveys clear usage context: use this when you need the real billing details behind advertised $/gr, including billing granularity, storage-while-stopped charges, egress fees, and known gotchas. It does not explicitly name alternative tools or exclusions, but the context is clear enough for an agent to select it appropriately.

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?

Without annotations, the description carries the full burden; it does convey that this is a query of current state and lists the data returned. However, it does not explicitly state that the call is read-only/non-mutating, and it omits caveats like what happens when a watch is not found or how 'recent events' is defined.

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 compact, front-loaded sentence with no filler. Each clause adds information about the returned state, and the length is appropriate for a tool that only requires watch_id.

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 one-parameter status query, the description names the main result groups (armed/tripped, last price, recent events, offer) and the resource, which is enough for an agent to select and invoke it. It is not quite a 5 because it leaves unstated the read-only guarantee and any error/edge-case behavior, but these are minor for this simple read 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?

The schema provides only the property name watch_id with zero description coverage, so the description must compensate. It never names watch_id, but saying 'state of a watch' clarifies that the parameter identifies the watch to query; it does not explain the id format or how to obtain it.

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 clearly identifies the resource as a watch and enumerates the returned state fields (armed/tripped, last price, recent events, best matching offer), making it easy to distinguish from sibling status tools like rental_status. It lacks an explicit verb such as 'Gets' or 'Returns,' so it is clear but not maximally 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?

The phrase 'current state' implies this tool is for reading watch status, but there is no explicit statement about when to prefer it over sibling tools or when not to use it. The watch-specific fields offer some context, but 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?

With no annotations provided, the description carries the full disclosure burden. It reveals that the calculation is deterministic, includes stated overhead, checks against the live order book, and refuses to invent throughput values. It does not describe the output format or pagination behavior, so it is not fully transparent.

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 dense but every sentence earns its place: the purpose question, the formula, the accepted input modes, precision options, throughput behavior, and assumptions disclosure. The most important information is 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?

For a tool with eight parameters, no annotations, and no output schema, this description is unusually complete: it states required input modes, formula components, optional enhancements, and output ordering. Still, it leaves limit and offer_class undocumented and does not describe the returned offer fields, requiring some agent inference.

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. It explains model vs params_b, precision choices, context/batch via the KV-cache formula, and tokens_per_s semantics. However, offer_class and limit are never explained, and there are no enums in the schema to fill that gap.

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 opens with a direct, specific question about model fit and rental cost, then states exact behavior: deterministic VRAM arithmetic compared against the live book with cheapest fitting offers first. This clearly differentiates it from sibling tools like search_offers, get_offer, and rent_offer.

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 provides clear invocation guidance: pass a known model slug or params_b, choose precision from fp16/int8/int4, and optionally pass your own measured tokens_per_s to enrich offers with usd_per_m_tokens. It does not explicitly name alternative tools or state when not to use it, so it stops short of a 5.

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