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by io.github.JTraversa
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Capacity stress score

capacity_stress
Read-onlyIdempotent

Paid (0.01 USDC per call). Capacity stress score, 0 (slack) to 100 (tight), for one GPU model on one day, with every component. Mean of the components available that day: availability (share of Shadeform and Lambda listings flagged available), ionet (deployable ÷ total units on io.net regional SKUs), supply (Vast on-demand offers vs the trailing 30-day median) and spot_ratio (hyperscaler SPOT fixing ÷ HYP fixing, 0.3 → 0 and 0.9 → 100). Each component carries its inputs.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dayNoYYYY-MM-DD; default latest.
gpuYesNormalized GPU model, e.g. `H100 SXM`. Example: H100 SXM

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
dayYes
gpuYes
scaleNo
scoreYes0 (slack) to 100 (tight)
componentsYes
methodologyNo
n_componentsNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
+  "$schema": "http://json-schema.org/draft-07/schema#",
+  "additionalProperties": true,
+  "properties": {
+    "components": {
+      "additionalProperties": true,
+      "properties": {},
+      "type": "object"
+    },
+    "day": {
+      "type": "string"
+    },
+    "gpu": {
+      "type": "string"
+    },
+    "methodology": {
+      "type": "string"
+    },
+    "n_components": {
+      "type": "number"
+    },
+    "scale": {
+      "type": "string"
+    },
+    "score": {
+      "description": "0 (slack) to 100 (tight)",
+      "type": "number"
+    }
+  },
+  "required": [
+    "gpu",
+    "day",
+    "score",
+    "components"
+  ],
+  "type": "object"
+}
  2. Changed1 schema field changed
    • changedInput schema / properties / gpu / description
      Previous value: -"Normalised GPU model, e.g. `H100 SXM`. Example: H100 SXM"New value: +"Normalized GPU model, e.g. `H100 SXM`. Example: H100 SXM"
  3. Changed1 schema field changed
    • changedInput schema / properties / gpu / description
      Previous value: -"Normalized GPU model, e.g. `H100 SXM`. Example: H100 SXM"New value: +"Normalised GPU model, e.g. `H100 SXM`. Example: H100 SXM"
  4. Changed1 schema field changed
    • changedInput schema / properties / gpu / description
      Previous value: -"Normalised GPU model, e.g. `H100 SXM`. Example: H100 SXM"New value: +"Normalized GPU model, e.g. `H100 SXM`. Example: H100 SXM"
  5. Changed1 schema field changed
    • changedInput schema / properties / gpu / description
      Previous value: -"Normalised GPU model, e.g. `H100 SXM`."New value: +"Normalised GPU model, e.g. `H100 SXM`. Example: H100 SXM"
  6. First observed

TDQS

A3.9/5.0
Behavior4/5

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

Annotations already cover the safety profile (readOnly, idempotent, non-destructive, openWorld), so the bar is lower; the description adds the non-obvious commercial constraint that each call costs 0.01 USDC. It also discloses how the number is derived (mean of components available that day) and names each component's source, which is context the annotations cannot carry.

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-loads price and the score's meaning before the component breakdown, and every clause carries information an agent would otherwise have to guess. The component formulas are dense but they define the returned number, so they earn their space.

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?

Output schema exists, so return-value explanation is not strictly required, yet the description still supplies the component semantics needed to interpret the score. With pricing, scope, scale, and inputs all covered, only the sibling-routing guidance is missing.

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 description coverage is 100% for both parameters (day format, normalized GPU model with example), so the schema does the heavy lifting and the baseline is 3. The description reinforces that the result is scoped to a single gpu/day but adds no format or naming detail 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?

States a specific resource (capacity stress score), its scale (0 slack to 100 tight), and its exact scope (one GPU model on one day). An agent can distinguish it from capacity_board, capacity_headline, and capacity_history without opening any schema.

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 scoping phrase 'for one GPU model on one day' and the per-call price imply when this tool applies, but no alternative sibling (e.g. capacity_board or capacity_history) is named and no when-not condition is given. Usage is inferable rather than instructed.

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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