list_gpusBrowse and filter supported GPUs and machines by memory, usable VRAM, and bandwidth to compare specs and pick hardware for running open-weight LLMs.
The GPUs and machines nodegrove.io covers: memory, the memory a runtime can use and bandwidth, from the makers' specs, with each one's page.
| Name | Required | Description | Default |
|---|---|---|---|
| search | No | Words to filter by, e.g. "qwen" or "24 GB". |
Changes observed during successful MCP inspections.
v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, non-destructive, and closed-world, so safety is fully covered by structured data. The description adds that values come from makers' specs and each entry links to a page, a modest but real behavioral detail. It does not address pagination or result size.
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 sentence, so nothing is wasted, but its grammar is awkward ('memory, the memory a runtime can use and bandwidth') and reads as a comma-spliced list rather than a clean, front-loaded statement. Adequate but not crisp.
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?
For a simple read-only list tool with full annotation coverage and no output schema, the description usefully previews the returned fields (memory, usable memory, bandwidth, page). The omission of result ordering or paging is minor at this complexity.
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?
Schema description coverage is 100% and the single 'search' parameter is well documented with examples ('qwen', '24 GB'). The description adds no filtering semantics beyond the schema, so the baseline 3 applies.
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 the specific resource (GPUs and machines nodegrove.io covers) and enumerates the attributes returned: memory, usable memory, bandwidth, and a per-GPU page. The catalog nature distinguishes it from estimation siblings like estimate_vram, though it never names them explicitly.
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?
No when-to-use guidance or exclusion criteria. The agent is not told how this catalog differs from list_models or when to reach for it instead of what_fits/estimate_vram, leaving the routing decision to inference from the name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.