Veqen Pricing: AI model costs

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. The description adds that results are reported both per call and per month and that token-based versus unit-based models are costed differently — useful behavioral context, though it says nothing about limits (max 10 models) or what happens with mixed/unsupported model lists.

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

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

A single dense sentence with no filler, and the core deliverable (cost per call and per month) is front-loaded before the input taxonomy. Slightly overloaded with parenthetical enumerations, but every clause carries 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?

An output schema exists, so return values need no explanation, and the input taxonomy is covered. However, for a 7-parameter analytical tool the description omits the multi-model limit, default assumptions (e.g. 100000 calls/month), and when this beats get_model_price or simulate_workload — a moderate 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 71%, just under the baseline threshold, and the description meaningfully compensates by explaining which inputs apply to which model classes: tokens for text, embedding and realtime models, units (images, seconds, characters, queries, pages) for the rest. That is genuine semantics beyond the field-level descriptions, though 'variant' and 'calls_per_month' get no treatment.

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 the resource precisely: cost of the same call across several models, per call and per month. The plural 'several models' implicitly distinguishes it from get_model_price (single price lookup) without naming the sibling, so differentiation is inferable rather than explicit.

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

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

No statement of when to use this versus get_model_price, simulate_workload, or estimate_switch. The multi-model framing hints at a comparison use case, but there are no exclusions or prerequisites, so an agent must infer routing from the sibling names alone.

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 and openWorldHint=false, so safety is covered. The description adds real context beyond that: rates are USD per million tokens, the field abbreviations (in, out, cr, cw, cw1h, ain, aout), tiering (batch, priority, long-context), and the unit-rate variant scheme for non-token billing.

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 core statement, then a dense but necessary glossary of rate fields and tiers. 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?

An output schema exists, so return values needn't be spelled out, yet the description still decodes the rate fields and variant notation. Only the relationship to sibling pricing tools is unaddressed.

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 both model and provider are already documented in the schema. The description adds only a general note that any model kind is supported, which is the correct baseline when the schema does the heavy lifting.

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 resource and outcome — pricing rates for a single model — and the sentence 'Models billed per image, second, character, query or page have a unit rate' makes the scope (any model kind) explicit. It does not name siblings like compare_models or list_models, so differentiation is left 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?

There is no when-to-use guidance, no prerequisites, and no named alternative. An agent must infer from the sibling list that compare_models handles multi-model comparison while this returns rates for one model.

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 readOnly and idempotent annotations already establish the safety profile, so the lower bar applies. The description usefully adds ordering ('cheapest first') and scope ('Current models'), but it does not explain the effect of include_retiring, pagination behavior, or what 'current' excludes beyond the default flag.

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 states the resource, ordering, and filter scope without wasted words. The enumeration of kinds is compact 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?

An output schema exists, so return values need not be described, but the parameter gaps remain significant: limit and include_retiring are undocumented despite zero schema descriptions. The absence of sibling differentiation also leaves the agent without enough context to route correctly among the many model-related tools.

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?

With 0% schema description coverage, the description carries the full burden, yet it only covers provider and kind, and for kind it merely repeats the enum values already in the schema pattern. It says nothing about 'limit' or 'include_retiring', leaving two of four parameters semantically 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 and resource ('Current models in the price book') and adds ordering ('cheapest first') plus filtering scope ('one provider or one kind'). It distinguishes itself implicitly from siblings like get_model_price and compare_models, though it does not name those alternatives explicitly.

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

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

The description implies usage through filter criteria ('for one provider or one kind') but never states when to choose this over compare_models, get_model_price, or simulate_workload. No exclusions or prerequisites are 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?

Annotations already declare readOnly, idempotent, non-destructive and closed-world, so the safety profile is covered. The description adds genuinely new behavioral context: costs are at list prices (no discounts), the projection assumes monthly growth, and results are comparative (rank among current models, other hosts, cheaper alternatives).

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

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

It is a single front-loaded sentence with zero filler, leading with what the tool does. The trailing appositive enumerating every output is long and partly duplicates the output schema, but it stays information-dense rather than rambling.

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 an output schema present and rich annotations, the description only needs to convey purpose and pricing/projection assumptions, both of which it does. Parameter-level detail gaps are the schema's responsibility here, so nothing critical for a correct call 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 coverage is only 33%, so more burden falls on the description, and it does clarify that calls and units are per call and that growth is a monthly projection. However, it says nothing about the months horizon, the 25-line cap, batch, variant/provider, or token fields, leaving most parameters to the schema alone.

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

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

The description states a specific verb and resource — simulating a month of calls across models at list prices — and enumerates the outputs it produces. It clearly separates itself from pure price lookup, but it never names a sibling like compare_models or estimate_switch to sharpen the 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?

The use case is implied by "a month of calls across any mix of models, at list prices," so an agent can infer this is for cost/volume planning. There is no explicit when-to-use, when-not-to-use, or routing to compare_models / estimate_switch / get_model_price, all of which live in the sibling list.

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