list_gpusEvery GPU model tracked, with its id, VRAM, how many providers list it and the median on-demand rate card in USD per GPU-hour.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
| Name | Required | Description | Default |
|---|---|---|---|
| gpus | No | ||
| count | No | ||
| cite_as | Yes | How to cite this answer: GPU Economy, the page, the data date. | |
| attribution | Yes | ||
| data_updated_at | Yes | Newest scrape behind the answer (ISO 8601). |
Changes observed during successful MCP inspections.
Output schema / properties / cite_asAdded value: +{
+ "description": "How to cite this answer: GPU Economy, the page, the data date.",
+ "type": "string"
+}Output schema / requiredPrevious value: -[
- "data_updated_at",
- "attribution"
-]New value: +[
+ "data_updated_at",
+ "attribution",
+ "cite_as"
+]Output schema / (root)Previous value: -nullNew value: +{
+ "properties": {
+ "attribution": {
+ "type": "string"
+ },
+ "count": {},
+ "data_updated_at": {
+ "description": "Newest scrape behind the answer (ISO 8601).",
+ "type": "string"
+ },
+ "gpus": {}
+ },
+ "required": [
+ "data_updated_at",
+ "attribution"
+ ],
+ "type": "object"
+}Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and openWorldHint=false, so the safety profile is covered. The description adds payload-level context (provider counts, median on-demand rate in USD per GPU-hour), but much of that restates return fields rather than disclosing behavior like ordering or completeness guarantees.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
A single front-loaded sentence with no filler; every clause describes a returned attribute of value. Nothing to trim.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With an output schema present, the description needn't enumerate return values, and its brief preview of fields is a useful orientation. For a no-argument read-only list tool this is essentially complete, lacking only notes on ordering or scope limits.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool takes zero parameters, so per the rubric the baseline is 4. There is no argument surface the description needs to explain.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
States a specific verb and resource ('list GPU models') plus the exact fields returned, so the agent knows this is a full-catalog enumeration. It doesn't explicitly contrast itself with close siblings like find_gpus or compare_gpus, but the 'every GPU model tracked' framing implies completeness versus a filtered search.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Usage is only implied: enumerating the whole tracked catalog. There is no explicit when-to-use statement, no when-not-to-use, and no named alternative among siblings such as find_gpus (search) or gpu_prices (rates).
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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