Check a transcription job. Returns status (queued / working / done / error) and metadata. Set include_transcript=true only once status is 'done' and you actually need the full text in context - transcripts of long videos are large.
| Name | Required | Description | Default |
|---|---|---|---|
| job_id | Yes | ||
| include_transcript | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond the annotations (readOnlyHint, idempotentHint), the description adds meaningful behavioral context: it specifies the exact status values returned, mentions that metadata is included, and discloses the potentially large payload size when include_transcript=true. This warning is valuable for an agent deciding whether to request the transcript.
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 concise sentences: the first states the purpose and return values, the second provides an actionable parameter guideline with a rationale. No waste, no repetition of schema details, and the most important caution (transcript size) is front-loaded in the second 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?
Given the simple two-parameter tool, no output schema, and present annotations, this description covers the essential aspects: what the tool does, what it returns, and a key behavioral warning about large transcript payloads. It is sufficiently complete for an agent to invoke the tool correctly and interpret the response.
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 fully compensates for include_transcript by explaining exactly when to set it to true and why (transcripts can be large). job_id is self-evident from the context of checking a specific job. This adds substantial meaning beyond the bare 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 opens with 'Check a transcription job,' which is a specific verb + resource pairing. It clearly distinguishes this tool from siblings (delete_job, list_jobs, submit_transcription) by focusing on retrieving a single job's status and metadata. The return value is explicitly enumerated (queued / working / done / error).
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 provides clear context for when to use the tool (checking a transcription job) and offers a specific parameter-level guideline: only include the transcript when status is 'done' and the full text is needed, warning about size. However, it does not explicitly contrast with sibling tools like 'list_jobs' for multiple jobs or 'delete_job' for removal, so alternatives are only implied.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool performs a distinct lifecycle action: submit, get, list, and delete. There is no overlap in purpose, and the descriptions clearly differentiate when each should be used.
All tools follow a consistent verb_noun pattern: submit_transcription, get_job, list_jobs, delete_job. The naming is predictable and aligns with the domain.
Four tools are well-scoped for a simple transcription job management service. Each tool earns its place without unnecessary bloat or missing essentials.
The core job lifecycle is covered: create, list, retrieve, and delete. The only minor gap is the lack of a cancel operation for queued or running jobs, but the provided flow is complete for typical use.