Tomorrow Central: Cloud Cost Sentinel

Check job status

get_job

Read-only

Check the status of a Tomorrow Central job.

Poll this after starting any scan. Status goes QUEUED → RUNNING → COMPLETED (or
FAILED). A typical scan takes 1-3 minutes. The response's `poll_after_seconds`
field is the minimum wait before polling again — respect it. Never start a second
scan while one is RUNNING; the platform coalesces duplicates onto the in-flight
job anyway (`coalesced: true`), and rate-limit errors include
`retry_after_seconds` telling you exactly how long to back off.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`job_id`	Yes

TDQS

A4.9/5.0

Behavior5/5

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

Annotations provide readOnlyHint=true, and the description goes far beyond by explaining status transitions, typical scan duration, the meaning of poll_after_seconds, coalescing behavior, and rate-limit retry_after_seconds. This enriches the agent's understanding of how the tool behaves without contradicting the read-only hint.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with the core purpose, then delivers essential operational details in a compact paragraph. Every sentence adds value—status flow, timing, polling behavior, and rate-limit handling—with no fluff or repetition.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Despite having no output schema, the description covers the key runtime behaviors and response fields (poll_after_seconds, coalesced, retry_after_seconds) that an agent needs. It gives a complete picture of how to invoke and interpret the tool in typical workflows.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, but there is only one required parameter (job_id) which is self-explanatory from its name and title. The description gives context that job_id comes from the scan initiation ('after starting any scan'), which partially compensates for the lack of explicit param documentation.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states a specific verb and resource: 'Check the status of a Tomorrow Central job.' It distinguishes by describing the status lifecycle (QUEUED → RUNNING → COMPLETED/FAILED), which differentiates from siblings like get_job_result that would return final outputs. The purpose is unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicit 'when to use' guidance: 'Poll this after starting any scan.' It also provides clear exclusions: 'Never start a second scan while one is RUNNING' and instructs to respect polling intervals. Although it doesn't name get_job_result, the context of polling vs. results is clear.

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

All Tomorrow Central: Cloud Cost Sentinel tools Inspect

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TDQS

A4.2/5.0

Disambiguation5/5

Each tool targets a distinct resource and action: connection lifecycle (create, verify, get, list), job lifecycle (run, get status, get result), and findings (list). Even get_job_result and list_cost_findings are clearly differentiated as raw vs. analyzed data, and whoami/list_tools_available serve metadata purposes.

Naming Consistency4/5

Most tools follow a consistent verb_noun pattern (create_, get_, list_, run_, verify_). The only outlier is 'whoami', which breaks the pattern but is a recognizable convention for account identification. Overall naming is predictable and readable.

Tool Count5/5

With 10 tools, the set is well-scoped for a cloud cost scanning platform. Each tool serves a clear purpose in the connection-scan-result workflow, with no redundancy or bloat.

Completeness3/5

The core scan workflow is covered (connect, verify, scan, get job, get findings), but there are notable gaps: no tool to delete/disconnect a cloud account, and no way to list past jobs or retrieve results without a prior job_id. These missing lifecycle/history operations could force agents to rely on external state or fail when context is lost.

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