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Tomorrow Central: Cloud Cost Sentinel

Look up real cloud list prices (no account needed)

price_lookup
Read-only

Look up current AWS on-demand list prices. No account, key or signup needed.

Use this to answer "what does X cost", to sanity-check a bill, or to price a
design before building it. Prices come from AWS's own published price files and
are refreshed on a schedule; the reply carries the book version so you can say
how current the number is.

`service` is one of the names from `list_priced_services` (for example `ec2`,
`rds`, `s3`, `lambda`, `ebs`, `data_transfer`, `nat_gateway`). `region` is an AWS
region id such as `ap-south-1` or `eu-west-1`.

`match` narrows within a service and is the parameter you will use most: pass
`t3.medium` for one instance type, `gp3` for one volume type, or leave it empty to
see everything that service publishes in that region. Matching is a plain substring
against the rate's dimensions, so partial values work.

`quantity` selects the pricing band for anything billed in tiers, which includes
S3 storage, data transfer out and Lambda duration. Pass the monthly amount in the
rate's own unit (GB, GB-seconds) to get the band that amount actually falls in.
Leaving it at 0 gives the first band, which OVERSTATES the cost of large volumes:
egress at 500 TB is materially cheaper per GB than at 100 GB.

These are list prices before any discount, private pricing, credit or committed-use
agreement, and they exclude tax. Treat them as an upper bound on a real bill.

If nothing matches, the reply says so and lists what that service does publish in
that region, so you can correct the term rather than guess again.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
matchNo
regionYes
serviceYes
quantityNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.3/5.0
Behavior5/5

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

Beyond the readOnlyHint annotation, the description discloses that no account or key is needed, that prices come from AWS's published files and are refreshed on a schedule, that the reply includes a book version for freshness, and that the tool lists alternatives when nothing matches. It also warns that leaving quantity at 0 OVERSTATES costs for large volumes, adding valuable behavioral context about default behavior.

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 is long but well-structured: purpose first, then usage, then parameter details, then caveats. Every paragraph adds value, and the most important information (scope, safety, no-account) is front-loaded. It could be tightened slightly, but the length is justified by the tool's complexity.

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?

The description covers nearly all agent needs: what it does, when to use it, parameter behavior, caveats about list prices, and the no-match fallback. It is missing an explanation of the limit parameter and a detailed return format, but since there is no output schema, the description carries that burden. The missing limit is a real gap, but the rest is thorough enough for correct invocation.

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

Parameters3/5

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

Schema coverage is 0%, so the description must fully explain parameters. It does well for service, region, match, and quantity, giving examples and explaining substring matching and pricing bands. However, it completely omits the 'limit' parameter, leaving it undocumented. This is a significant gap given the description is the only source of parameter semantics.

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 verb-resource pair ('Look up current AWS on-demand list prices') and explicitly names the use cases ('what does X cost', sanity-check a bill, price a design). It distinguishes itself from siblings like estimate_cost by clarifying it returns raw list prices, not discounts or estimates, and references list_priced_services for valid service names, separating it from that helper.

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

Usage Guidelines4/5

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

The description states when to use the tool ('Use this to answer...') and provides practical context like verifying bills or pricing before building. It implicitly differentiates from estimate_cost by noting list prices exclude discounts and are an upper bound. It does not explicitly name alternatives or say when NOT to use it, but the context is clear enough for an agent to select it appropriately.

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

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