compute.wick.pics

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

With no annotations provided, the description carries the full behavioral burden. It discloses the polling interval, the firing condition, the re-arming mechanism (when price climbs 2% back over the line), the watch id as the only key, and the webhook behavior. This is exceptional transparency for a tool description.

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?

Every sentence is information-dense and earns its place. The description front-loads the core concept, then details the monitoring behavior, return value, webhook option, and re-arm logic. No filler or repetition of schema titles.

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, the description explicitly states the return value ('Returns the watch with its id'). It also covers polling cadence, webhook/notification behavior, re-arm condition, and all relevant input semantics. Nothing an agent needs to select and invoke this tool correctly is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 0% description coverage, so the description must fully compensate. It does: it explains gpu, offer_class (optional), max_price_per_gpu_hr (the threshold), and webhook_url (to be POSTed on each trip). It provides semantic context the schema lacks entirely.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a specific verb ('Create') and resource ('price watch (tripwire)') and explains exactly what it does: fires when price drops to or under a threshold. It is clearly distinguishable from siblings like watch_status and delete_watch because it uniquely describes the creation mechanism and firing condition.

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 when to use the tool: creating a price watch with a max price threshold. It also gives guidance about how the watch is checked and how to interact with it afterwards (poll with watch_status or give a webhook_url). It does not explicitly state when not to use it or name alternatives like search_offers, but the usage context is sufficiently 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 clearly signals a destructive operation by using the word 'Delete'. With no annotations present, the description carries the full burden, but it does not disclose whether deletion is permanent, reversible, or has side effects on related resources.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single sentence with no wasted words. It is immediately readable and upfront about the core operation and the key parameter.

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 single-parameter delete operation, the description is nearly complete. It tells the agent what action to perform and what input is required. It does not describe the return value or side effects, but no output schema exists and the operation is straightforward.

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%, but the description partially compensates by clarifying that watch_id is the identifier of the watch being deleted. Still, it adds little beyond the parameter name and title, and provides no format, example, or additional context for what makes a valid watch_id.

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 ('Delete'), a clear resource ('a watch'), and the selection mechanism ('by id'). This cleanly differentiates it from siblings like create_watch and watch_status, all at a glance.

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 intended use is implied directly by the purpose: use this tool to delete a watch by its id. However, the description does not explicitly mention alternatives, prerequisites, or when not to use it, leaving the when-to-use guidance to inference.

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. 'Fetch' implies a read-only operation, which is useful, but the description does not disclose what happens when the offer_id is not found, whether errors are raised, or what the return shape is. Adequate but minimal for an unannotated 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?

A single, direct sentence with no filler. The core purpose is front-loaded, and the provenance hint is appended without redundancy.

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 single-parameter lookup, the definition is largely complete. It explains what the tool does and where the parameter value comes from. The main missing piece is return-value or not-found behavior, but the tool is simple enough that this is a minor gap.

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 does implicitly explain that offer_id identifies an offer and that the id comes from search_offers, which adds some provenance. However, it adds no format, example, or guidance beyond that.

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 clear verb ('Fetch') and resource ('one offer') with an explicit id lookup. The parenthetical '(from search_offers)' gives provenance and distinguishes it from sibling search_offers and other tools.

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 note '(from search_offers)' implies the correct usage context: call this after search_offers to fetch a specific offer. It does not explicitly state when not to use it, but the context is clear for 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?

No annotations are provided, so the description carries the behavioral disclosure burden. It does add useful context about what the points represent and the output format, but it does not disclose behaviors like how the hours parameter affects the time window, whether results are aggregated, or what the default behavior is.

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 carry a large amount of context: the metric is defined in plain language, and the exact point shape is listed. No fluff or redundant restatement exists.

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?

Although the tool is simple with one optional parameter, the description's lack of parameter semantics leaves an agent without enough information to confidently choose the right time range. The output format is partly covered by 'points are ...', but the API's behavior over time remains under-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?

The schema describes only the field name, type, and default for hours, with 0% coverage from the description. The description never explains what 'hours' controls, how it affects the returned time series, or why the default is 168.

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 specifies a clear, measurable resource: the idle-capacity index over time, capturing the share of listed GPU capacity that is interruptible. It also defines the exact point format [ts, idle_share, idle_offers, total_offers], making the tool's function unambiguous and distinguishing it from siblings like price_history or market_stats.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no guidance on when to use idle_history versus alternatives such as market_stats or price_history. The usage context is only implicit: it is a history tool for idle capacity, but no explicit when-to-use or when-not-to-use guidance is given.

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

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

There are no annotations, so the description must carry behavioral context. It does convey that this is a live, current snapshot and names the expected output fields. It does not explicitly disclose that it is read-only/side-effect-free, nor does it explain error conditions or usage limitations, which would strengthen transparency.

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 main concept, 'Live market snapshot', and then gives a structured list of the included data points. It contains no filler, redundant phrasing, or unnecessary background.

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 zero-parameter surface and simple snapshot behavior, the description covers the essential context an agent needs: what kind of data is returned and which market segments it spans. Some terms, such as 'freshness/health' and 'fee_bps', are not fully defined, so an agent might still need to inspect actual response shape, but the tool is usable as described.

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 accepts zero parameters and the schema description coverage is 100%, so there are no parameter details for the description to add. The description correctly focuses on what the snapshot contains rather than on nonexistent inputs. This matches the zero-parameter baseline.

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 specifies exactly what the tool returns: a live market snapshot with total offers, GPU models, idle-capacity share, provider freshness/health, cheapest price per model, and current fee_bps. This clearly identifies the aggregate market-overview purpose. It does not start with an explicit verb, but the resource and scope are still evident.

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 'live / snapshot' framing and aggregate field list imply this is the current market-overview tool, as opposed to offer-level tools, history tools, or watch-management tools. However, it does not explicitly state when to prefer this tool over siblings or mention any exclusions, so the usage guidance remains implied rather than direct.

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 transparency burden and handles it well by explaining the response shape and bucketing behavior (hourly then 6-hourly). It does not mention failure cases or rate limits, but for a pure read-style history tool the disclosed behavior is useful and specific.

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 well-structured: target object, required input convention, enum values, response fields, and time bucketing. Every sentence contributes, and the most important differentiation ('for one GPU model') 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?

Given there is no output schema and no annotations, the description still provides return fields, provider grouping, and temporal aggregation, which is strong coverage. The main gap is the undocumented hours parameter and the lack of a brief comparison to siblings, but the tool is simple enough that the description is nearly 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?

The description adds real meaning for gpu_model and offer_class, since the schema has 0% coverage and no enums. However, the hours parameter is never mentioned, and because the schema provides no description, the agent cannot confidently know how hours controls the lookback window or interacts with the stated bucketing logic.

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 says the tool returns per-provider price history for one GPU model, gives an example value, and makes clear this is history rather than a snapshot or aggregate. It does not explicitly distinguish itself from siblings like market_stats or idle_history, so it falls just short of a 5.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

It implies the correct usage by requiring an exact gpu_model name that must come from search_offers or market_stats, which tells the agent to resolve model strings first. However, it does not explicitly state when to choose this tool over siblings like market_stats or idle_history, nor when not to use 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 behavioral burden. It does disclose that results are ranked cheapest-first by price_per_gpu_hr and that it searches 'live' offers, but it does not mention side effects, how default/zero values are interpreted, or expected behavior for empty results.

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 tight and front-loaded, with one purposeful sentence for each of purpose, key parameter semantics, and return ordering. No filler or redundant information is present.

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 search tool with 8 optional parameters and an output schema present, the description covers the key disambiguating concepts and return ordering. It does not enumerate every filter behavior, but the parameter names and defaults make the remaining behavior reasonably clear.

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 does so for gpu, offer_class, and country with examples and enums, but leaves limit, provider, min_vram_gb, min_reliability, and max_price_per_gpu_hr mostly self-explanatory by name only; max_price_per_gpu_hr default of 0 is ambiguous (no filter vs zero price).

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 a specific action and resource: 'Search live GPU offers.' It also gives concrete filter semantics (gpu matches model names, offer_class values, ISO-2 country codes) and return ordering, which distinguishes it from sibling tools like get_offer and price_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?

The intended use is implied by the first sentence and the detailed filter examples, but there is no explicit guidance about when to use this tool versus siblings like get_offer, market_stats, or price_history. No exclusions or alternative conditions are stated.

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

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

With no annotations provided, the description carries the burden of explaining behavior. It clearly conveys a read-only snapshot operation and lists the data the caller can expect. However, it does not mention what happens for invalid or missing watch IDs, how fresh the data is, or any side effects or special access requirements.

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 purpose and then lists the returned data components. Every phrase contributes useful information and there is no redundant 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 simple one-parameter read tool, the description is largely complete: there is no output schema, but the description enumerates the return contents. Minor gaps around invalid IDs, freshness guarantees, and permission requirements remain, but they are not critical for choosing or 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?

Schema description coverage is 0%, and the description does not compensate by explaining watch_id semantics beyond the obvious. It says 'state of a watch' but does not clarify what identifiers are valid, where watch_id comes from, or any expected format, so the schema remains the only meaningful information.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description identifies the resource (a watch) and specifies the contents of the status lookup: armed/tripped state, last price, recent events, and current best matching offer. It is specific and distinguishes itself from sibling tools like price_history and get_offer, though it lacks a direct verb such as 'gets' or 'returns.'

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 'right now' implies this is for current/point-in-time status rather than historical analysis, giving some usage context. However, the description never explicitly says when to prefer this over alternatives like get_offer, idle_history, or price_history, nor does it state any exclusions.

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