Tomorrow Central: Cloud Cost Sentinel

Rate a Tomorrow Central result

submit_rating

Idempotent

Rate a result you were given, from 1 (useless) to 5 (exactly what was needed).

`rating_token` comes back alongside the result itself, from get_job_result or
list_cost_findings. Do not construct one: a token you invent will be rejected. Each
token can be rated once.

A low rating is more useful than a high one, so rate honestly rather than kindly.
Add a `comment` saying what was wrong; without one, a low score says nothing
actionable. For anything that needs a fix rather than a score, use report_feedback.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`rating`	Yes
`comment`	No
`rating_token`	Yes

TDQS

A4.5/5.0

Behavior4/5

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

Annotations already declare non-read-only and idempotent, but description adds crucial behavior: invented tokens are rejected, tokens are single-use, and low ratings need comments. No contradiction with annotations.

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

Conciseness4/5

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

The description uses three short paragraphs, each with a distinct purpose: what it does, token rules, and rating guidance. It avoids fluff while providing necessary behavioral context.

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

Completeness4/5

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

For a simple rating tool with no output schema, the description covers token provenance, one-time use, rating scale, comment guidance, and the alternative tool. It's sufficiently complete for an agent to invoke correctly.

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 the description explains the meaning of rating_token (where it comes from, don't construct), the rating scale (1-5), and the purpose of comment. This compensates well for the schema's lack of descriptions.

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 opens with a clear directive: 'Rate a result you were given, from 1 (useless) to 5 (exactly what was needed).' It explicitly distinguishes from sibling report_feedback by stating 'For anything that needs a fix rather than a score, use report_feedback.'

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?

It provides explicit usage context: rating_token is sourced from get_job_result or list_cost_findings, must not be invented, and is single-use. It also tells agents to add a comment for low ratings and refers to report_feedback for fixes.

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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