get_export_jobCheck an export job started with create_export_job. Status moves queued, then running, then completed; a completed job carries result.url pointing at the finished artifact. Poll no faster than the retry_after hint on the previous response.
| Name | Required | Description | Default |
|---|---|---|---|
| job_id | Yes | The id returned by create_export_job. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already mark the tool as read-only and idempotent. The description adds meaningful behavioral detail beyond that: the status progression (queued, running, completed), the result.url field on completion, and the retry_after rate-limiting hint. This is exactly the kind of context an agent needs to poll correctly.
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 sentences with no filler: the opening identifies the operation, the second explains the lifecycle and output, and the third provides actionable polling guidance. Every sentence earns its place and the key 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?
For a single-parameter polling tool with no output schema, the description covers the essential contract: how to identify the job, what states it goes through, what a successful result looks like, and how fast to poll. 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 coverage is 100%, and the job_id parameter is already described as 'The id returned by create_export_job.' The description reinforces this by saying the job must come from create_export_job, but it doesn't add new parameter-level information beyond what the schema provides, so baseline 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?
States a specific verb ('Check') and resource ('an export job started with create_export_job'), making the tool's role unambiguous. It also distinguishes itself from create_export_job by explicitly framing this as following up on a job created there.
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 job must have been started with create_export_job, and polling should respect the retry_after hint. It doesn't explicitly list when-not-to-use or name alternatives, but the pairing with create_export_job and the polling guidance give sufficient direction.
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 targets a clearly distinct piece of information or action: compatibility checks, system requirements, pricing, features, FAQ, releases, and export jobs. Even the pair check_mac_compatibility and get_system_requirements is separated by one asking for a decision about a specific Mac and the other returning raw requirements.
Tool names consistently follow a verb_noun pattern using get_, list_, create_, and check_. Singular nouns pair with single-item resources and plural nouns with collection endpoints, making the set predictable.
Nine tools is well-scoped for a product information and export server. Each tool covers a meaningful part of the surface without redundancy or padding.
The tool set covers the full likely question space for the Yappy app: compatibility, requirements, pricing, features, FAQ, latest release, release history, and bulk corpus export. The async export pair provides a complete path to documentation content without leaving dead ends.