corpusAI Cloud Pricing

Reserved price history

reserved

Read-onlyIdempotent

Paid (0.01 USDC per call). Reserved / reservation price history of one instance type (AWS standard RIs 1y/3y no-upfront/all-upfront; Azure reservations 1y/3y). Effective hourly USD with any upfront amortized over the term, as change-point series per ri_type (ri_1y_no_upfront_standard, ri_1y_all_upfront_standard, ri_3y_no_upfront_standard, ri_3y_all_upfront_standard; GCP cud_1y, cud_3y). AWS: Linux, shared tenancy, history back to 2016 for the original three regions, weekly refresh from the AWS Price List API. Azure: all-upfront reservations from the public Retail Prices API, daily, since 2026-09-13. GCP: resource-based committed-use discounts from the Cloud Billing Catalog (vCPU, RAM and GPU commitment SKUs), daily, since 2026-09-13.

Input Schema

TableJSON Schema

Name	Required	Description
`region`	Yes	Region slug (for example `us-east-1`, `us-east` for Azure, `us-central1` for GCP). Example: us-east-1
`ri_type`	No	Restrict to one ri_type.
`instance`	Yes	Instance / machine / SKU type name exactly as listed by /instances (for example `c5.large`, `n2-standard-4`, `D2s_v5`). Example: c5.large
`provider`	No	Provider: `aws` (default), `azure` or `gcp`.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`note`	No
`region`	No
`series`	No
`instance`	No
`provider`	No

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedSep 28, 2026

changedOutput schema / (root)

Previous value: -nullNew value: +{
+  "$schema": "http://json-schema.org/draft-07/schema#",
+  "additionalProperties": true,
+  "properties": {
+    "instance": {
+      "type": "string"
+    },
+    "note": {
+      "type": "string"
+    },
+    "provider": {
+      "type": "string"
+    },
+    "region": {
+      "type": "string"
+    },
+    "series": {
+      "items": {
+        "additionalProperties": true,
+        "properties": {
+          "changes": {
+            "items": {
+              "additionalProperties": true,
+              "properties": {
+                "date": {
+                  "description": "Day the price took effect.",
+                  "type": "string"
+                },
+                "price": {
+                  "description": "USD per hour.",
+                  "type": "number"
+                }
+              },
+              "required": [
+                "date",
+                "price"
+              ],
+              "type": "object"
+            },
+            "type": "array"
+          },
+          "count": {
+            "type": "number"
+          },
+          "current": {
+            "additionalProperties": true,
+            "properties": {
+              "date": {
+                "description": "Day the price took effect.",
+                "type": "string"
+              },
+              "price": {
+                "description": "USD per hour.",
+                "type": "number"
+              }
+            },
+            "required": [
+              "date",
+              "price"
+            ],
+            "type": "object"
+          },
+          "ri_type": {
+            "type": "string"
+          }
+        },
+        "type": "object"
+      },
+      "type": "array"
+    }
+  },
+  "type": "object"
+}

First observedSep 26, 2026

TDQS

A4.4/5.0

Behavior5/5

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

Annotations already cover read-only/idempotent/non-destructive, and the description goes well beyond them: paid-per-call pricing, data provenance (AWS Price List API, Azure Retail Prices API, GCP Cloud Billing Catalog), refresh cadence (weekly/daily), coverage start dates, and the amortization semantics of the returned values.

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?

Front-loaded with the cost notice and purpose, and every sentence carries substantive content (provenance, cadence, semantics). It is dense and the long parenthetical enumerations of ri_type tokens and provider dates require careful reading, but little is wasted.

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?

For a complex multi-provider time-series tool, the description supplies the provenance, refresh cadence, coverage windows, and value semantics needed to trust and call it, while the output schema handles the return shape.

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, but the description adds real meaning: it maps each ri_type token to its provider-specific meaning (AWS 1y/3y no-upfront/all-upfront, GCP cud_1y/cud_3y), documents region slug conventions per provider, and states the AWS scope (Linux, shared tenancy) that the schema does not.

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?

States a specific verb+resource ('reserved / reservation price history of one instance type') with the data model (change-point series per ri_type, amortized effective hourly USD). It is clearly separable from snapshot-style siblings like reserved_snapshot and ondemand_snapshot by the 'history' scope and series framing.

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 scope is implied (one instance type, one of six ri_type series), and the 0.01 USDC-per-call cost warns about usage, but there is no explicit when-to-use vs alternatives guidance and no sibling (e.g. reserved_snapshot, term) is named as the alternative.

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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