vetted-consumer

Can I run it?

can_i_run_it

Will a given local LLM run on given hardware? Returns fit, the best quant that fits, theoretical tok/s, and real owner-measured tok/s where available.

Input Schema

TableJSON Schema

Name	Required	Description
`model`	No	Model name, e.g. 'Llama 70B', 'gpt-oss-120B', 'Qwen 32B'. Use list_models to see known names.
`mxfp4`	No	True if the model ships natively in MXFP4 (e.g. gpt-oss)
`context`	No	Context window in tokens (default 8192)
`total_b`	No	For an unlisted model: total parameters in billions
`unified`	No	True for unified-memory machines (Macs, Strix Halo, CPU+RAM)
`vram_gb`	No	For custom hardware: VRAM or unified memory in GB
`active_b`	No	For an unlisted model: active params in billions (= total for dense, less for MoE)
`hardware`	No	Hardware name/id, e.g. 'rtx-3090', 'Mac 128GB', 'Strix Halo'. Use list_hardware to see known ones.
`kv_precision`	No	KV cache precision (default f16)
`bandwidth_gbps`	No	For custom hardware: memory bandwidth in GB/s

Schema Changelog

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

First observedJun 23, 2026

TDQS

A4.4/5.0

Behavior3/5

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

No annotations are provided, so the description carries full behavioral burden. It describes the output (fit, quant, tok/s) but lacks disclosure of read-only nature, side effects, or safety considerations. For a query tool, this is adequate but not comprehensive.

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, front-loaded sentence that efficiently conveys the tool's function and return values. Every word adds value with no redundancy.

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

Completeness5/5

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

Given the absence of an output schema, the description adequately explains return values (fit, quant, tok/s, real tok/s) and the default context. Combined with full schema coverage, it provides sufficient information for an agent to use the tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, and the description adds valuable context beyond parameter names, such as recommending list_models/list_hardware for known values and explaining default contexts. This significantly enhances usability.

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's purpose: it checks if a given local LLM runs on given hardware and returns specific outputs (fit, quant, tok/s). This distinguishes it from sibling tools like recommend_hardware or cheapest_hardware_for_model, which have different goals.

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

Usage Guidelines4/5

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

The description implies usage when you have a specific model and hardware in mind, and references related tools (list_models, list_hardware) for input preparation. However, it does not explicitly state when not to use this tool or provide exclusions.

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

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