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corpusAI Cloud Pricing

Hourly spot bars

spot_hourly
Read-onlyIdempotent

Paid (0.01 USDC per call). Hourly time-weighted OHLC bars for one instance type. Per-zone series are bucketed by UTC hour with time weighting (a price counts for as long as it was in force), then collapsed across zones: open/close/avg are the median across zones, high is the max, low is the min. Window is capped at 366 days.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
toNoEnd of the window (exclusive), ISO 8601 UTC. Default: now.
fromNoStart of the window (inclusive), ISO 8601 UTC. Default: 30 days before `to`.
regionYesRegion slug as listed by /catalog (for example `us-east-1` for AWS, `us-central1` for GCP, `us-east` for Azure). Azure slugs map to `eastus`, `westus2`, `westeurope`. Example: us-east-1
instanceYesInstance / machine / SKU type name exactly as listed by /instances (for example `c5.large`, `n2-standard-4`, `D2s_v5`). Example: c5.large
providerYesCloud provider. One of `aws`, `gcp`, `azure`.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.4/5.0
Behavior5/5

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

Beyond the read-only and idempotent annotations, the description discloses meaningful behavior: the call costs 0.01 USDC, bars are time-weighted by how long a price was in force, per-zone series are collapsed with median/max/min logic, and the window is capped at 366 days. These are non-obvious facts the annotations and schema do not provide.

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?

Three sentences deliver cost, granularity, scope, aggregation methodology, and window limits with no filler. Decision-relevant facts are front-loaded before the technical collapse logic.

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 read-only query tool with fully documented parameters and no output schema, the description explains both the call constraints and the returned OHLC aggregation semantics. It omits explicit response-shape or pagination details, but those are minor given the tool's simplicity and the rich annotations.

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 100%, so the baseline is 3. The description adds value by constraining from/to to a maximum 366-day window and emphasizing that exactly one instance type is queried, which clarifies the scope of the instance parameter beyond the schema.

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 states the deliverable clearly: hourly time-weighted OHLC bars for one instance type. The granularity and single-instance scope distinguish it from siblings like spot_daily, spot_monthly, and spot_latest without needing to open their schemas.

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

Usage Guidelines3/5

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

The description implies usage via the word 'hourly' and the 'one instance type' constraint, and it adds a 366-day window cap. However, it never explicitly routes the agent to alternatives such as spot_daily or spot_monthly or states when not to use this tool, so the guidance remains inferential.

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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