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vetted-consumer

by io.github.TheBaronofAI

Cheapest hardware for a model

cheapest_hardware_for_model

The cheapest catalogued, buyable machine that runs a given model at Q4 with the requested context.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modelNoModel name, e.g. 'Llama 70B', 'gpt-oss-120B', 'Qwen 32B'. Use list_models to see known names.
mxfp4NoTrue if the model ships natively in MXFP4 (e.g. gpt-oss)
contextNoContext window in tokens (default 8192)
total_bNoFor an unlisted model: total parameters in billions
active_bNoFor an unlisted model: active params in billions (= total for dense, less for MoE)

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

C2.9/5.0
Behavior2/5

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

With no annotations, the description must fully disclose behavioral traits. It only states the tool returns a 'catalogued, buyable machine' but does not explain data sourcing, update frequency, pricing accuracy, or any side effects. This is insufficient for an agent to assess reliability.

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?

The description is a single, front-loaded sentence with no wasted words. It conveys the essential idea economically. However, it could be more structured by grouping constraints or clarifying scope, preventing the need for a longer description.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given 5 parameters, no output schema, and no annotations, the description is too sparse. It does not explain the output format, data source reliability, or how the 'cheapest' is determined (e.g., price vs. availability). The agent needs more context to use this tool effectively.

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%, so the schema already documents all 5 parameters. The description adds no extra meaning beyond the schema. It mentions Q4 and context, but those are inherent in the tool's purpose, not parameter-level details. Baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly identifies the tool's purpose: finding the cheapest hardware for a given model with specific constraints (Q4 quantization, context). It uses a specific verb 'cheapest' and resource 'hardware for a model', which differentiates it from siblings like 'recommend_hardware' or 'compare_hardware'.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is provided on when to use this tool versus its siblings. The description does not mention alternatives or when not to use it. Given the context signal of 7 sibling tools, the lack of usage context is a significant gap.

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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TDQS

A3.7/5.0
Disambiguation4/5

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.

Naming Consistency3/5

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.

Tool Count5/5

9 tools is well-scoped for a domain of hardware/model comparison, covering all key actions without being excessive.

Completeness4/5

Covers main workflows: compatibility check, cost analysis, recommendations, and listings. Lacks advanced filtering or detailed hardware specs, but core functionality is present.

Resources