LLM Configurator

List models a machine can run

models_for_hardware

Read-onlyIdempotent

Use this when the user asks what models a given machine can run. Needs a hardware id from search_catalog. Returns up to 20 catalogue models ranked for that machine, each with a fit verdict and a speed range, optionally filtered to one use such as coding. It covers only models in the catalogue, at one quantisation and context length per call, and leaves out models that do not fit.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "hardware_id": "<hardware_id>"
    },
    "name": "models_for_hardware"
  }
}

Input Schema

Name	Required	Description	Default
`limit`	No	Maximum models, 1 to 20. Default 10.
`use_case`	No	Only list models tagged for this use. One of coding, chat, reasoning, agents, vision. Omit for all models.
`hardware_id`	Yes	Hardware id from search_catalog.
`quantisation`	No	Weight quantisation. One of Q2_K, Q3_K_M, Q4_K_M, Q5_K_M, Q6_K, Q8_0, F16. Default Q4_K_M.	Q4_K_M
`context_length`	No	Context window in tokens that the KV cache is sized for. 512 to 262144. Default 4096.

Output Schema

Name	Required	Description
`url`	Yes
`models`	Yes
`summary`	Yes	One or two plain-language sentences stating the answer.
`hardware`	Yes
`use_case`	Yes	The use-case filter applied, or null.
`data_as_of`	Yes	Date the bundled catalogue was last updated. Not the time of this call.
`quantisation`	Yes
`context_length`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedOct 2, 2026

TDQS

A4.3/5.0

Behavior5/5

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

Annotations already declare read-only, idempotent and closed-world, and the description adds real substance on top: the result is capped at 20 ranked models with a fit verdict and speed range, only one quantisation and context length apply per call, and non-fitting models are excluded. That scope disclosure is exactly what an agent needs and is not derivable from the schema.

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?

Three sentences, all load-bearing, with the trigger and prerequisite front-loaded before the return shape and the coverage caveats. No filler or repetition of schema content.

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?

With a full input schema, an output schema and safety annotations already present, the description supplies the remaining context: the trigger condition, the prerequisite, and the ranking/fit/speed semantics of the result. Nothing an agent needs to invoke it correctly is missing.

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 every parameter including defaults and enum values is already documented. The description adds only a light gloss ("optionally filtered to one use such as coding") and mentions the single-quantisation/context constraint, which the schema already conveys through its enum and defaults.

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?

States a specific verb and resource in user-intent terms: list the models a given machine can run. The direction is clearly the inverse of check_hardware_fit, and it names search_catalog as the source of the hardware id, but it never explicitly contrasts itself with those siblings.

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?

Gives an explicit trigger ("Use this when the user asks what models a given machine can run") plus a hard prerequisite (hardware id from search_catalog). It does not say when to prefer check_hardware_fit or compare_hardware instead, so it stops short of full alternative routing.

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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