Makernice

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

Annotations already flag destructiveHint=true and readOnlyHint=false, so the safety profile is covered. The description adds substantive behavior the annotations don't: the held amount is released in full and cancellation is impossible once generation has begun, which tells the agent exactly what state change and failure mode to expect.

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, zero filler, with the action and side effect front-loaded and the exclusion immediately following. Every clause carries information the agent needs.

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

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

For a single-parameter destructive tool with full schema coverage and an output schema, the description covers purpose, side effect, and the failure condition. Only the exact error response for a non-queued job is unstated, a minor gap given the output schema exists.

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 the single parameter is documented as 'job_id returned by generate'. The description adds no further meaning about the identifier, 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 gives a specific verb and resource ('Cancel a job') plus a scope qualifier ('still queued') and the side effect ('release the held amount in full'). It clearly separates this tool from siblings like generate or check_job by stating exactly what state the job must be in.

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 explicitly states the precondition (job must still be queued) and the exclusion ('Jobs that already started generating cannot be canceled'), which is strong when/when-not guidance. It stops short of naming an alternative path for already-started jobs, so it isn't a full 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?

Annotations already declare readOnlyHint=true, destructiveHint=false and closed-world, so the safety profile is covered. The description adds real behavioral context beyond that: a blocking wait of up to 55 seconds and a 24-hour expiry on result links, both of which affect how an agent should sequence calls.

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

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

Three short sentences, zero filler, with the primary product of the call front-loaded and the practical caveat (link expiry) last. Every sentence 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?

An output schema exists, so return-value documentation is not required, yet the description still names the three key returns. For a two-parameter status-check tool with full annotation coverage, nothing an agent needs in order 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 coverage is 100% and both parameters are already documented, so the baseline is 3. The description clarifies the intent of wait_s (block until finished rather than a bare sleep) but adds no format or constraint detail beyond the schema's '0-55' range.

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 — 'Get the status, result links and charged amount of a job' — and even enumerates the return payload, so an agent can distinguish it from cancel_job or generate immediately.

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 supplies conditional guidance for wait_s ('set wait_s to wait for the job to finish') and a re-call rule for expired links, but it never states the core trigger (call this after generate) or any exclusion versus siblings like cancel_job. Usage is implied rather than 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?

Annotations already declare readOnlyHint, destructiveHint=false and openWorldHint=false, so the safety profile is structured data. The description adds the non-obvious guarantee that nothing is charged, which is real value beyond the annotations. It stops short of saying whether invalid or unsupported parameter combinations produce an error vs. a fallback estimate.

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 tightly written sentences, zero waste, with the core capability and the no-side-effect guarantee front-loaded ahead of the usage hint.

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 annotations plus schema cover most remaining ground. Minor gap: the description doesn't note that the estimate only reflects parameters it can price, or that the estimate should correspond to a matching generate 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 coverage is 100%, so the schema carries the parameter burden and baseline is 3. The description only gestures at model, resolution and duration; it does not explain that the same parameters must mirror the subsequent generate call, nor clarify the two accepted input shapes (model-parameter object vs {template, images}).

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 — 'Calculate the price in USD of a generation request' — and explicitly negates side effects ('without creating anything or charging'). It also names the sibling 'generate' so the agent can separate the two without opening 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?

Gives a clear when-to-use ('compare models, resolutions and durations before calling generate'), which effectively routes the agent to this tool as a pre-flight step. No explicit when-not conditions, but the 'before calling generate' framing covers the main decision boundary.

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?

Goes well beyond the annotations: states that the price is charged (locked) at submit time, that jobs exceeding max_cost_usd are refused, that failed jobs are not charged, and that reference images must be public https URLs. The billing semantics — the most consequential behavior for a non-destructive but costly operation — are disclosed directly.

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?

Five short sentences, each carrying a distinct fact (charge, refusal, job_id, polling, failure billing, URL constraint) with 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?

For a paid async generation tool with an output schema, the description covers the missing pieces: charging model, failure handling, polling target, and URL validity. Nothing an agent needs in order to call it correctly is absent.

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 already 100%, so the baseline is 3; the description adds real meaning on top by explaining that max_cost_usd acts as a hard refusal threshold and that image URLs must be public https. It does not explain the model/template input variants, which the schema handles.

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

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

Specific verb and resource: 'Start a video or image generation job and charge the locked price from the balance.' An agent can distinguish it cleanly from check_job, cancel_job, estimate_cost and list_models without opening a 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?

Explicitly routes the agent to check_job for results and states the max_cost_usd refusal condition, which effectively tells the caller when the job will not run. It does not say whether estimate_cost should be called first or how to pick a model, but the core workflow is 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?

Annotations already declare readOnlyHint=true, openWorldHint=false and destructiveHint=false, so the safety profile is covered. The description adds two pieces of real context beyond that: values are in USD and one figure is scoped to 'this connection', but it says nothing about staleness, scoping of the account, 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?

A single front-loaded sentence with the resource first and the returned figures after; every clause earns its place 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?

For a parameterless read with an output schema, annotations covering read-only behavior, and a full return-shape definition downstream, the description says enough to select and call it. Only the connection-scoping nuance and its relationship to siblings are 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?

The tool takes no parameters, so there is nothing for the description to disambiguate; the baseline for a zero-parameter tool applies. The enumerated return fields go slightly beyond what is required here.

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?

Names a specific verb and resource ('Get the account balance') and enumerates the returned figures including 'this connection's per-job limit', which distinguishes it from estimate_cost and list_trends. It stops short of explicitly routing the agent away from any sibling, so it is clear but not fully differentiated.

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 the tool to call when checking funds or limits before a job. There is no explicit when-to-use, when-not-to-use, or named alternative (e.g. estimate_cost for pre-flight cost projection).

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=true, destructiveHint=false and openWorldHint=false, so safety is covered. The description usefully clarifies that this is a passive link-retrieval that delegates the actual top-up to the user, but it says nothing about link lifetime, whether the link is user-specific, or any auth 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?

A single front-loaded sentence that identifies the resource first and the user-facing purpose second, with no redundant restatement of the title or annotations. 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?

For a zero-parameter read tool with an output schema and full annotation coverage, the description supplies what is needed to select and call it. The only meaningful omission is a pointer to the sibling get_balance for agents deciding between reading a balance and offering a top-up.

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 meaning to convey and the baseline of 4 applies. Nothing in the description is needed to explain inputs, and nothing misleads about them.

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 (get) and resource (the link to the Makernice wallet page), plus the outcome the link enables (the user adds balance themselves). It implicitly contrasts with the sibling get_balance by making clear this does not read or move money, but it never names a sibling explicitly, so it stops just short of full differentiation.

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 'where the user can add balance themselves' implies the use case: hand the user a self-service top-up URL rather than performing a deposit. There is no explicit when-to-use instruction, no mention of the get_balance alternative, and no note on preconditions such as an authenticated user or existing account.

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=true, destructiveHint=false and openWorldHint=false, so the safety profile is covered; the description adds real value by disclosing the two operating modes (catalog listing vs single-model detail) and that prices are quoted in USD. It stops short of noting rate limits or whether the catalog is static.

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 filler, with the primary listing behavior front-loaded and the follow-up workflow hint second. Every clause carries information an agent needs.

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

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

With annotations covering safety, an output schema covering the return shape, and only one optional parameter, the description supplies everything an agent needs to call it correctly and to know why it would. Minor gap: it doesn't clarify how granular 'modes' are or whether the catalog is stable across calls.

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 the single `model` parameter already carries a description in the schema. The description restates that passing it yields full parameter details; that meaning is already in the schema, so the added value is marginal beyond the baseline for high-coverage schemas.

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 ('List the available video and image models') and enumerates the returned fields (modes, resolutions, durations, unit prices in USD). This clearly separates it from siblings like estimate_cost and generate, which consume rather than describe model metadata.

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 sequences the workflow: pass `model` to get full parameter details for that model *before* calling estimate_cost or generate. It names the downstream tools and the condition that selects the detail mode, leaving nothing 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?

Annotations already declare readOnlyHint=true, destructiveHint=false, and openWorldHint=false, so the safety profile is covered. The description adds useful behavioral context by explaining that results include pricing per resolution and that template ids feed into generate. It does not describe ordering or pagination, but those are minor for a zero-parameter listing tool with an output schema.

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 tight sentences with no filler. The resource and its definition are front-loaded, followed by the practical next step for using the result.

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 tool is simple, parameterless, read-only, and has an output schema, so the description does not need to explain return values in detail. It covers what is listed, the pricing context, and how to use the result. Minor details such as ordering or whether the list is exhaustive are 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?

This tool has zero parameters, so there are no parameter semantics to explain. The baseline for a parameterless tool is 4, and the description appropriately focuses on the returned template data and how to use the template 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 uses a specific verb and resource: 'List trending video templates'. It also defines what a trend template is and states that results include total price in USD per resolution. That is enough for an agent to distinguish this from sibling tools such as list_models or estimate_cost.

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 downstream usage: 'Use the template id in generate with { template, images }.' This tells the agent when the returned data is useful, though it does not explicitly contrast this tool with list_models or other listing tools.

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