Kingy AI Facts

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

Annotations already declare readOnlyHint=true, openWorldHint=false, idempotent and non-destructive, so the safety profile is covered. The description adds genuinely useful non-obvious detail: each verdict carries the exact file and format, the runtime, the measured speed, and a provenance tag distinguishing Kingy-measured from third-party-reported from estimated — which tells the agent how much to trust the answer. It does not describe the response shape or behavior for unknown models beyond the 'Not tested yet' verdict.

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

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

Two sentences, front-loaded with the verdict concept before the supporting detail about file/format/runtime/provenance. Dense but every clause carries information; the parenthetical machine list is slightly long but earns its place by defining the enum domain.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

There is no output schema, so the description must carry the return-value burden, and it does: verdict tiers, per-machine results, file/format, runtime, speed, and provenance. For a simple two-parameter lookup this is close to sufficient; only the unknown-model / no-data path is left implicit.

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

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

Schema coverage is 100%, so baseline would be 3, but the description goes further by glossing the six enum values in human terms (16GB laptop, 24GB/32GB GPU PC, 64GB Mac, 128GB Mac or Strix Halo, 512GB Mac Studio), which materially helps the agent map a user's hardware to an enum value. It adds no guidance on the model string format, which the schema already handles with an example.

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: a verdict on whether a model runs on your own hardware, and enumerates the exact verdict scale (Yes, Yes but slow, Barely, No, Not tested yet) plus the machine buckets. No sibling tool (check_model_access, get_model_prices, etc.) covers local-run feasibility, and the description makes that distinction obvious without opening either schema.

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

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

Usage is strongly implied by the purpose ('should you run it at home?'), and the machine parameter's 'omit for all' behavior is clear. However, the description never explicitly contrasts this with the nearest sibling, check_model_access, which an agent could plausibly confuse with a 'can I run this' question; there are no stated exclusions or antecedents.

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

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

Annotations already declare readOnly, idempotent, non-destructive and closed-world, so the safety profile is covered. The description adds genuinely useful behaviour beyond that: the four-value answer vocabulary (yes/conditional/no/unknown), the assessment dimensions (availability, licence, contract, regulatory) and sources, plus a 'Not legal advice' caveat. It still omits freshness/dating of the underlying data and confidence handling for the 'unknown' outcome.

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?

Compact and front-loaded: the core question comes first, followed by the return shape and then a short disclaimer. The only real waste is the product-name prefix and the run-on first clause, which pack several qualifiers into one dense 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 nine-parameter, no-output-schema tool, the description does well to enumerate the return values and the dimensions behind them. It falls short by not explaining how the optional qualifiers (sector, revenue band, audience, decision impact, data sensitivity) alter the outcome, which is exactly the information an agent needs to decide whether to supply them.

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 78% with eight enums, so the baseline is 3. The description does add meaning by glossing route values as direct API, third-party provider or open weights, and expanding jurisdiction codes to US, Canada, EU, UK, but it says nothing about the non-required inputs (sector, revenue, data, audience, decision) that materially change the verdict.

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 evaluation: whether a given model may be used, parameterised by purpose, route and jurisdiction, and it names the four-value answer scale. The branded 'Kingy Can I Use:' prefix is slightly noisy, and it never names a sibling such as can_i_run_locally, which is the nearest overlap (open weights vs local execution).

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

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

Usage is implied by the framing ('whether a specific model can be used for a purpose, through a route, in a jurisdiction'), so an agent can infer this is the licensing/availability lookup. However there is no explicit when-to-use versus when-not guidance, no mention of when to prefer can_i_run_locally or get_model_prices, and no prerequisites 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?

Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false and openWorldHint=false, so safety and determinism are covered structurally. The description adds useful context beyond them: the data is US-priced, each entry carries a price check date and official sources, signaling freshness and provenance. It still says nothing about result size, ordering, or how 'documented access' for a feature is determined.

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 sentence with no filler, front-loading the geographic scope and the core data returned. It is a noun fragment rather than an action statement, which slightly weakens clarity but keeps it tight.

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 read-only, zero-required-parameter comparison tool with no output schema, the description covers the data domain, geographic scope, and provenance of the returned entries. It omits what the comparison output looks like and how many plans are returned, but those are minor gaps given the tool's simplicity.

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

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

Schema description coverage is 100%, so all three optional filters (feature, provider, max_monthly_usd) are already documented in detail with examples and the annual-plan monthly-equivalent rule. The description adds no additional parameter semantics, so the baseline of 3 applies.

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

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

The description names a specific resource set: US prices, features and conditions for consumer AI subscriptions, and enumerates the covered providers (ChatGPT, Claude, Gemini/Google AI, Copilot). It clearly scopes to consumer subscription plans as opposed to model API prices, which hints at differentiation from get_model_prices, but it never states the comparison/list action explicitly and does not name siblings 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?

There is no when-to-use guidance at all: nothing says to call this instead of get_model_prices, get_usage_limits or check_model_access, and no exclusions or prerequisites are given. The agent must infer from the phrase 'consumer AI subscriptions' that this is not an API-price tool.

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

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

Annotations already declare readOnly, idempotent, non-destructive and closed-world, so safety needs no repetition. The description adds real behavioral context beyond that: pricing comes from 'currently verified standard rates', long-context tiers are applied, and models without a verified price are returned as unavailable with a reason. It still doesn't say what the output looks like or which currency is used.

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

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

Two dense sentences with zero filler, front-loading the action and scope, followed by the pricing and edge-case behavior. Nothing is redundant with the annotations or schema.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With full schema coverage, annotations, and an unambiguous calculation model, the only real gap is that no output schema exists and the description doesn't state the shape of the returned estimate or its currency. That gap is minor but not trivial for a comparison tool whose value is in the numbers it returns.

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

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

Schema coverage is 100%, so the schema documents every parameter and the baseline is 3. The description nonetheless clarifies the interaction between parameters with the formula 'tokens per request times requests', which explains how requests combines with input/output token counts, and notes the long-context tier behavior tied to those token counts.

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

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

The description gives a specific verb and resource ('estimate the cost of a workload') and scopes it to 'up to six models' using verified standard rates, so an agent can tell this is a calculator rather than a raw price lookup like get_model_prices. It never names a sibling tool, so the differentiation is inferred rather than stated.

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

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

Usage is only implied: an agent can infer this is for budgeting or pre-run cost comparison, but the description never states when to use it over get_model_prices or compare_subscription_plans, nor any exclusions. No explicit when/when-not 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?

Annotations already declare readOnly, idempotent, non-destructive and closed-world, so the safety profile is covered. The description adds genuinely new context: the data is curated from official vendor sources, and each record carries deadline, required action and sources, which tells the agent how trustworthy and actionable results are. No pagination or result-volume behavior is mentioned, keeping it below a 5.

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

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

Two tight sentences, front-loaded with the returned resource types and followed by the use case. No filler, though the long comma-separated enumeration in the first sentence is dense and slightly harder to scan than a short lead clause would be.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With five optional parameters, no output schema and a closed-world read-only profile, the description covers purpose, result shape and provenance adequately. It would be stronger if it stated the default result count or noted that unfiltered queries return the whole reviewed set, but nothing an agent needs to call it correctly is missing.

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

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

Schema description coverage is 80% and the schema already explains query format, status semantics ('open' means upcoming or active) and vendor examples. The description adds no input-side meaning at all — its field list describes response shape rather than the five parameters — so the baseline 3 applies.

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

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

The description concretely enumerates what is returned — retirements, deprecations, breaking API changes, migration deadlines, access and price changes — each with model ids, deadlines, required action and sources. That is far more specific than the title, but it does not differentiate from siblings: 'access and price changes' overlaps with check_model_access and get_model_prices, and list_ai_shutdowns is never addressed.

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?

A single usage sentence exists ('Use it to check whether a model id in your code is being retired'), which gives a clear primary trigger. However, it covers only one narrow scenario, gives no when-not guidance, and names no alternative, leaving the agent to guess between this tool and check_model_access or list_ai_shutdowns.

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

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

Annotations already declare readOnly, idempotent, non-destructive, and closed-world, so the safety bar is met structurally. The description adds genuinely new behavioral context: rows in held or expired state carry no confirmed price, which prevents misreading a blank price as free. It omits any note on result ordering or size, but no output schema exists and the return shape (price, context limits, sources) is summarized.

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

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

Two sentences, zero padding, with the scope and provider list front-loaded and the state caveat immediately after. Nothing repeats the title verbatim and every clause carries information an agent would otherwise have to guess.

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 three-parameter read-only lookup with no output schema, the definition covers what is returned, which providers, and the crucial state semantics. The remaining gap is routing against sibling cost/plan tools, which would make it fully self-sufficient.

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

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

Schema description coverage is 100%, so all three parameters (model, provider, current_only) are already documented with examples and the 'omit to list all' default. The description's mention of 'current' state reinforces current_only but adds no syntax or format detail beyond the schema. Baseline 3 applies when the schema does the parameter work.

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

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

The description names a specific resource and scope: 'Current per-token API prices for the AI models Kingy verifies (OpenAI, Anthropic, Google)'. It also states what accompanies the price (context limits, official sources). It does not explicitly distinguish the read-only lookup from siblings like estimate_api_cost or compare_subscription_plans, so sibling differentiation is left implicit.

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 purpose is implied as a price lookup, and the note that only 'current' rows have a confirmed price hints at when rows are meaningful, but the description never says when to use this versus estimate_api_cost (cost projection) or compare_subscription_plans. No explicit exclusions or alternative routing is given despite several adjacent tools.

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

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

Annotations already declare the read-only, idempotent, non-destructive, closed-world profile, so the description's burden is lower. It still adds meaningful context beyond them: results carry 'review status and source,' signalling that figures are curated and may vary in verification level — useful for judging trustworthiness. It does not disclose coverage gaps or staleness windows.

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: the first front-loads what the tool returns and the product coverage, the second delivers the invocation hint. No filler, no restatement of the title, and the parenthetical field list is compact.

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 read-only, no-output-schema lookup with two optional filters and rich annotations, the description supplies enough: payload fields (messages, credits, rate limits, reset windows), provenance metadata, and the no-arg behavior. Minor gap: it doesn't state whether plan alone is a valid query or how the two filters combine.

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

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

Schema coverage is 100% and both parameters are optional, so the baseline is 3. The description earns above baseline by explaining the zero-argument case ('Call with no product to list covered products') — meaning not present in the schema, which otherwise reads as a plain filter. It stops short of clarifying product+plan interaction or plan-only queries.

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

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

The description gives a specific verb+resource ('Documented usage limits ... for AI products and plans') and enumerates concrete products (ChatGPT, Claude, Gemini, Copilot, Cursor, etc.), so the agent knows exactly what domain of data is returned. It does not explicitly distinguish itself from siblings like compare_subscription_plans or get_model_prices, leaving the boundary between 'limits' and 'plans/prices' to inference.

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?

One concrete invocation pattern is given: 'Call with no product to list covered products.' This implies the optional-parameter behavior usefully. However, there is no guidance on when to reach for this tool versus compare_subscription_plans or get_model_prices, and no statement about whether 'plan' can be used alone as a filter.

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

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

Annotations already declare readOnlyHint, idempotentHint, closed-world, and non-destructive, so the safety profile is fully covered. The description adds useful content context: entries are dated and include citations plus reviewed alternatives. It says nothing about ordering, pagination beyond the schema's limit cap, or the shape of results, which is the remaining gap.

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

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

A single front-loaded sentence that identifies the collection and its contents with no filler sentences. The 'Kingy AI Graveyard' branding is slightly promotional but still conveys the scope efficiently.

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 read-only, zero-required-parameter browse tool with no output schema, the description adequately tells the agent what entries look like (dated, cited, with alternatives). It omits default result size, sort order, and how the group filter changes output, which an agent would need to call it confidently.

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

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

Schema coverage is 67%; limit and query already carry descriptions in the schema. The description does add value by enumerating tools, models, and transitions, which maps directly onto the otherwise undocumented 'group' enum. It gives no detail on query matching behavior or the default ordering, so it stays at 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 names a specific resource and its three content types: dated shutdowns of AI tools, retirements of AI models, and business transitions, with citations and alternatives. It implicitly separates itself from siblings like find_model_changes by focusing on retired/discontinued products rather than changes, though the listing verb comes only from the tool 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?

There is no explicit when-to-use guidance, no statement of when not to use it, and no reference to alternatives such as find_model_changes or search_kingy. An agent must infer from the phrase 'AI Graveyard' that this is a browsable catalog of retired products rather than a change feed or general search.

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

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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false and openWorldHint=false, so the safety profile is fully covered elsewhere. The description's added value is the return payload (titles, URLs, dates, short excerpts), which matters because no output schema exists. It says nothing about ranking, result ordering, or what an empty result 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?

Two short sentences with zero filler; the capability comes first and the return shape second. Nothing is repeated from the title or annotations.

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 two-parameter read-only search with full schema coverage, the description covers what is searched and what is returned, which is the right level of detail. Minor gaps: no note on result ordering or on how broad/narrow the index is (articles vs. tools pages).

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

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

Schema description coverage is 100%, so both the query example and the limit default (6, max 10) are already documented in the schema. The description adds no search-syntax guidance, operator support, or advice on choosing a limit, so baseline 3 applies.

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

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

States a specific verb ('Search') and a specific resource scope ('kingy.ai articles, original tests, comparisons and tools'), plus what comes back. An agent can tell this is a content search over the kingy.ai site rather than a model/price/access lookup, though the description never names a sibling to contrast with.

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

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

Usage is implied by the content scope: reach for this when the answer likely lives in kingy.ai editorial content. There is no explicit when-not-to-use guidance and no mention of alternatives such as compare_subscription_plans or get_model_prices, which an agent could easily confuse with 'comparisons' and 'tools' mentioned here.

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