get_used_gpu_pricesCurrent typical used-GPU prices for local-AI rigs (eBay Browse API median asking + hand-verified, monthly).
| Name | Required | Description | Default |
|---|---|---|---|
| gpu | No | Optional name/id filter, e.g. "3090" |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It discloses data source, verification method, and update frequency, which is good. However, it omits details like rate limits, whether results are real-time, or any potential staleness beyond the monthly update cycle.
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?
The description is a single focused sentence of 20 words that front-loads the core purpose. Every word adds value, with no redundancy or filler.
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?
Given the lack of an output schema, the description should hint at the return format (e.g., list of prices, single price, GPU names). It mentions 'prices' but does not clarify structure, leading to potential confusion for the agent. The tool is simple, but completeness is lacking.
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% for the single parameter, so baseline is 3. The description adds context about the tool's purpose but does not elaborate on the parameter's behavior beyond what the schema says. No additional semantics are provided.
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?
The description clearly specifies the verb ('get'), resource ('used-GPU prices'), context ('local-AI rigs'), and data source/frequency ('eBay Browse API median asking + hand-verified, monthly'). It effectively distinguishes from siblings like 'cost_compare' or 'list_hardware' which cover different price types.
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?
The description implies when to use (when needing used GPU prices) but lacks explicit guidance on when not to use or alternatives among siblings. No exclusion criteria or comparison with tools like 'recommend_hardware' is provided, leaving some ambiguity.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Tools have mostly distinct purposes, though there is some overlap between cheapest_hardware_for_model and recommend_hardware, which both suggest machines for a model. Can_i_run_it also partially overlaps with these. However, descriptions clearly differentiate them.
Most tools follow verb_noun pattern (list_hardware, compare_hardware), but can_i_run_it is a question and cost_compare is noun_verb, creating minor inconsistency.
9 tools is well-scoped for a domain of hardware/model comparison, covering all key actions without being excessive.
Covers main workflows: compatibility check, cost analysis, recommendations, and listings. Lacks advanced filtering or detailed hardware specs, but core functionality is present.