Get the status and result of a crawl or search job. Terminal states: SUCCESS, FAILURE and REVOKED; PENDING/STARTED must be polled again.
| Name | Required | Description | Default |
|---|---|---|---|
| job_id | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and idempotentHint=true, and the description adds important behavioral details beyond those: terminal states (SUCCESS, FAILURE, REVOKED) versus non-terminal states (PENDING/STARTED), and the need to poll again. This is valuable, non-redundant context.
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 concise sentences. It front-loads the core purpose, then adds the most critical behavioral note (polling and terminal states) without any filler or redundancy.
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 a simple single-parameter schema and an output schema present, the description covers the needed operational semantics: what the tool does, what states exist, and that polling is required. The annotations cover safety, so no further behavioral disclosure is needed.
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%, and the description does not explain the job_id parameter, such as where to obtain it or its expected format. Although the parameter name is self-explanatory and only one exists, the description fails to compensate for the absence of schema descriptions, so the agent must infer the meaning.
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 ('Get') and clearly identifies the resource ('status and result of a crawl or search job'). It distinguishes from siblings like start_crawl and start_search by focusing on retrieving an existing job, and even names the poll-able states.
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 usage context: it is for retrieving job status and indicates that PENDING/STARTED states must be polled again, implying repeated calls. It does not explicitly state when not to use this tool or name alternatives, but the sibling list and phrasing make the intended use obvious.
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 has a clearly distinct purpose: start_crawl and start_search initiate different async operations, get_job polls their status, scrape_url returns content synchronously, and get_usage handles account details. No two tools overlap in functionality.
All tool names follow a consistent verb_noun pattern: get_job, get_usage, scrape_url, start_crawl, start_search. The verbs (get, scrape, start) clearly indicate the action, and the nouns identify the target resource.
Five tools cover the core workflows of this scraping/search service: two async starters, one poller, one synchronous scraper, and one usage checker. This is well-scoped without unnecessary redundancy.
The core lifecycle is covered: start async jobs, poll for results, retrieve synchronous content, and check account balance. Minor gaps exist, such as no explicit cancel/revoke job tool or a way to list historical jobs, but these are not critical for basic usage.