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

With no annotations present, the description carries the full burden and does so thoroughly: it discloses asynchronous job submission, default polling, wait-timeout semantics (still_processing=true), the need to poll get_cut_status with the returned id, and detailed billing rules including no charge on failure. This is far more transparent than a simple 'cuts a video' 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?

Three tightly packed sentences deliver purpose, process, return value, pricing, and timeout behavior with no filler. Every sentence earns its place and 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?

Despite 17 parameters and no output schema, the description covers all operational essentials: what is returned, how the async job behaves, what to do on timeout, and cost/failure semantics. The fully-covered schema handles parameter-level details, so nothing critical is missing 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 description coverage is 100%, so all 17 parameters are already documented in detail. The description adds operational context around job lifecycle and pricing but does not add per-parameter meaning beyond what the schema states, so the baseline score of 3 is appropriate.

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?

Opens with a specific verb and resource: 'Cut a clip (or clips) from a YouTube video and return its download URL.' This clearly distinguishes it from siblings get_cut_status (polling job status) and get_account_balance (billing). The additional detail about submitting a cut job and returning produced status refines rather than obscures the core 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?

Provides clear context: the tool submits a cut job, polls by default, and explicitly directs the agent to get_cut_status when a wait-timeout occurs. It does not fully enumerate when to avoid this tool or mention get_account_balance, but the key alternative behavior is explicitly covered.

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 adds useful context by specifying 'spendable' credit and 'hourly cap', but doesn't explicitly confirm the call is side-effect-free, discuss error scenarios, or note whether it hits an external service. For a no-parameter getter this is adequate but not exceptional.

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 names the verb and both return values with zero wasted words. 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 zero-parameter, no-output-schema tool, the description covers the essential ground: what is returned and for whom. The only minor omission is specifics about the return format/units of the balance, but given the tool's simplicity and the clear sibling context, nothing critical 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?

With 0 parameters, baseline is 4. The description does clarify that the balance is for 'the API account' (not a passed-in user), which adds meaningful semantic context about whose balance this is. The schema is empty so there is nothing else to document.

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 ('Return') and names the exact resource ('API account's spendable credit balance and hourly cap'). It clearly distinguishes itself from the sibling tools (cut_youtube_video, get_cut_status), which are entirely different domains.

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 its usage through context—it's the only read-only account-balance tool among the siblings. However, it never explicitly explains when to choose this over alternatives or mentions prerequisites like API key configuration. The differentiation from siblings is not explicit but is strongly implied.

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 burden. It discloses the return states and the conditional download_url, which is good. However, it doesn't mention potential rate limits, error handling, or whether the job status is final, but for a simple status check this is adequate.

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-loaded with the primary purpose, and includes necessary details without fluff. Every sentence adds value.

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 status-check tool with one parameter and no output schema, the description is complete. It covers the states, the download_url condition, and the usage context. Minor gaps like error handling are not critical for this 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 coverage is 100% and the parameter is well-described in the schema. The description adds context by explaining the job_id is returned by cut_youtube_video, which is helpful but not essential beyond the schema.

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 checks a cut job's status by its id, lists the possible states, and mentions the download_url condition. It distinguishes itself from siblings by referencing cut_youtube_video and the polling use case.

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 states when to use this tool: to poll a job started with cut_youtube_video(wait=false) or one that timed out. This provides clear context and differentiates from the sibling tools.

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