LLM Configurator

Get a model's specifications

get_model_specs

Read-onlyIdempotent

Use this when the user asks about one model's size, architecture, context window or licence. Needs a model id from search_catalog. Returns total and active parameters, weight size per quantisation, context length, licence, release date and sources. It does not say whether the model fits any machine; use check_hardware_fit for that.

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": {
      "model_id": "<model_id>"
    },
    "name": "get_model_specs"
  }
}

Input Schema

Name	Required	Description	Default
`model_id`	Yes	Model id from search_catalog.

Output Schema

Name	Required	Description
`url`	Yes
`model`	Yes
`sources`	Yes
`summary`	Yes	One or two plain-language sentences stating the answer.
`data_as_of`	Yes	Date the bundled catalogue was last updated. Not the time of this call.
`quantisations`	Yes	Weight size per quantisation. Excludes KV cache and overhead, which depend on context length.

Schema Changelog

Changes observed during successful MCP inspections.

First observedOct 2, 2026

TDQS

A4.5/5.0

Behavior4/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, openWorldHint=false, destructiveHint=false, so safety is covered. The description still adds value by disclosing the required input provenance (model_id must come from search_catalog) and the scope limit (does not answer hardware-fit questions). It does not mention rate limits or fallback behaviour, keeping it short of a 5.

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?

Four tight sentences with no filler: trigger first, prerequisite second, return contents third, scope exclusion last. Each sentence carries distinct decision-relevant information.

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 an output schema present, annotations covering the safety profile, and a single fully documented parameter, the description supplies everything else an agent needs: when to call it, what id to supply, what it returns, and which sibling to use instead for hardware-fit questions.

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% on the single required parameter and the schema already states 'Model id from search_catalog.', which the description only repeats. Per the high-coverage baseline, 3 is correct since the schema carries the parameter semantics.

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?

States a specific verb+resource (get a model's specifications) and enumerates the exact attribute domains it covers: size, architecture, context window, licence. It also explicitly carves out the sibling boundary against check_hardware_fit, so an agent can route without opening either schema.

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

Usage Guidelines5/5

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

Gives an explicit trigger ('when the user asks about one model's size, architecture, context window or licence'), a prerequisite (a model id from search_catalog), and a clear exclusion with the named alternative ('It does not say whether the model fits any machine; use check_hardware_fit for that').

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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