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vetted-consumer

by io.github.TheBaronofAI

Recommend hardware

recommend_hardware

Ranked list of catalogued, buyable machines that run a model at the requested context, cheapest first, with an optional budget cap.

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)
budgetNoOptional max price in USD
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)
kv_precisionNoKV cache precision (default f16)

Schema Changelog

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

  1. First observed

TDQS

A3.7/5.0
Behavior3/5

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

No annotations are provided, so the description bears full transparency burden. It discloses the ranking criterion ('cheapest first') but does not mention what happens if no machines match, auth requirements, or rate limits. The behavioral scope is partially clear.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single sentence of 18 words, front-loading the key information (ranked list, buyable machines, context, cheapest-first, budget cap). No wasted words.

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

Completeness3/5

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

Given 7 parameters and no output schema, the description only states the return is a ranked list, but does not specify the fields in each machine entry, pagination, or how the ranking is derived beyond price. Adequate for a simple list but missing return structure details.

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 coverage is 100%, so baseline is 3. The description adds no extra meaning beyond the schema; it only reiterates the optional budget. The description does not provide parameter-level detail beyond what is already in 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?

The description clearly states the tool returns a 'ranked list of catalogued, buyable machines' that run a model at a given context, sorted cheapest first. The verb 'recommend' and resource 'hardware' are explicit, and it distinguishes from siblings like 'can_i_run_it' (binary check) and 'cheapest_hardware_for_model' (single result).

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 description implies usage when needing a ranked list of hardware for a model, but does not explicitly state when to use this tool versus alternatives like 'compare_hardware' or 'cost_compare'. No exclusions or prerequisites are mentioned.

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