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

With no annotations, the description carries full behavioral burden: it discloses the three things that are inspected, which is useful, but says nothing about whether checks hit the network, whether failures throw vs. report, or what the result looks like. For a zero-parameter diagnostic that is an acceptable but not rich disclosure.

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 waste; the check list is front-loaded and the directive to run it first is a natural trailing clause. Nothing could be removed without losing meaning.

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 trivially simple, parameterless check tool the description is nearly sufficient, but with no output schema and no annotations it leaves the agent guessing about the result shape and the remediation path when a component is missing. Adding one clause on the pass/fail outcome would close the 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?

The tool takes zero parameters, so the baseline of 4 applies — there is nothing for the description to clarify beyond confirming that no configuration input is required, which its call-with-nothing framing implicitly does.

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+resource in 'check whether this computer's environment is complete' and enumerates what is verified (ffmpeg, Groq key, translation backend). This clearly distinguishes it from the transcribe_* siblings, which all deal with running transcriptions rather than verifying the host environment.

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?

'裝完先跑這個' (run this first after installation) gives an explicit trigger condition for invoking the tool. It offers no exclusions or named alternatives, but the sibling set is a different workflow so no disambiguation is really needed.

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 full behavioral burden and discloses very little. It implies a read-only listing but says nothing about ordering, pagination, retention window, or whether results are scoped to the caller – all relevant for a job-listing 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 compact sentence with the resource and returned status front-loaded and no filler. It is appropriately sized for a simple list tool, though it is terse to the point of under-specification.

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 explained. However, the undocumented limit parameter and the absent usage context leave the agent without enough to call the tool confidently in edge cases.

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% and the single parameter (limit, default 20) is never mentioned in the description, so the agent learns nothing about how many jobs are returned or whether the list is truncated. The description does not compensate for the coverage gap.

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 clear verb (列出/list) and resource (轉字幕工作/transcription jobs) plus the returned content (各自狀態/each one's status). It does not differentiate itself from the sibling transcribe_status, which also concerns job status, so an agent must infer the distinction (list all vs. query one).

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 statement of when to use this tool versus transcribe_status or transcribe_start, nor any prerequisite or scope conditions. The 'recent jobs' framing implies browsing, but nothing is explicit.

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 full burden and does well: it discloses that this is an async background job that returns a job_id immediately, where output files land (<filename>.en.srt / .zh.srt / .zh-TW.srt), that translator='groq' needs a Groq key, and what force does. It does not cover auth beyond the Groq key, rate limits, or failure/error 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?

Front-loaded with the core function and the async behavior, then use cases, then a clean parameter block. The parameter list is long but each entry earns its place given 0% schema coverage; wording is dense but not redundant.

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 7-parameter tool with no annotations and no output schema, the description supplies the async model, the return value (job_id), the on-disk outputs, and the follow-up tool. Nothing essential for correct invocation 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 0%, so the description must compensate and it does thoroughly: each of the 7 parameters (paths, language, translate, translator, model, context, force) is explained with meaning, accepted values, defaults, and practical effects (e.g. language 'zh' triggers Traditional-Chinese conversion and proofreading; context stabilizes proper-noun translation). This is far beyond what the bare schema provides.

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 (convert video/audio into subtitles or transcripts) plus the key behavioral fact that it runs as a background job returning a job_id immediately. This clearly distinguishes it from siblings transcribe_status and transcribe_list.

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 '用在：' line enumerates concrete scenarios (subtitle overlays, audio-to-text, meeting/lecture transcription, Chinese subtitle translation) that give strong positive guidance. It also routes the agent to transcribe_status for progress, though it never states when NOT to use it or contrast against the sibling tools explicitly.

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 full behavioral burden. It usefully discloses that the tool returns progress, subtitle file paths, and recent execution messages, but it does not state that this is a read-only operation, whether it has side effects, or any permission or rate-limit considerations.

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 tool's purpose and returned data with no filler whatsoever.

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 tool with no annotations, no output schema, and 0% schema description coverage, the description is insufficient. It covers the general return content but leaves parameter semantics, especially log_lines, and usage alternatives unexplained, so an agent lacks enough context to invoke 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 description coverage is 0%, so the description must compensate for two undocumented parameters. It implies selecting a specific job via '某支' but does not name job_id or explain its format, and it never mentions log_lines or what that parameter controls.

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 (查/check) and resource (某支轉字幕工作/a subtitle transcription job), and it enumerates the returned information: progress, output subtitle path, and recent execution messages. It clearly distinguishes itself from transcribe_list and transcribe_start by focusing on one job's status, though it does not explicitly name those siblings.

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 when-to-use or when-not-to-use guidance is provided. The description does not mention alternatives such as transcribe_list for listing jobs or transcribe_start for creating them, so the agent must infer the routing from the tool name alone.

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