LLM Configurator

Find hardware or a model

search_catalog

Read-onlyIdempotent

Use this first when the user names a GPU, Mac, mini PC or language model and you need its id for the other tools. Matches names and common aliases in a fixed catalogue of local-LLM hardware and models. It does not return specs, fit or speed, and it does not search the web.

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": {
      "query": "<query>",
      "kind": "<kind>"
    },
    "name": "search_catalog"
  }
}

Input Schema

Name	Required	Description
`kind`	Yes	Search hardware or models. Required.
`limit`	No	Maximum matches, 1 to 10. Default 5.
`query`	Yes	Hardware or model name as the user wrote it, e.g. "4090", "m4 max 64", "llama 3.3 70b".

Output Schema

Name	Required	Description
`kind`	Yes
`matches`	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.

Schema Changelog

Changes observed during successful MCP inspections.

First observedOct 2, 2026

TDQS

A4.7/5.0

Behavior4/5

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

Annotations already declare read-only, idempotent and closed-world, so the safety profile is covered. The description adds real behavioral context beyond them: the corpus is a fixed catalogue (closed world confirmed, not web), matching includes common aliases, and the result deliberately omits specs/fit/speed. Nothing about pagination or result shape, but the output schema covers returns.

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?

Two sentences, no filler. The usage trigger and prerequisite come first, followed by the negative scope, so the most decision-relevant information is front-loaded.

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?

An output schema exists, so return values need no explanation; the description instead covers the two things the schema cannot: when to reach for this tool first and what it deliberately will not return. Nothing needed 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.

Parameters4/5

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

Schema description coverage is 100%, so the baseline is 3, and the schema already supplies enum values, bounds and example queries. The description earns above baseline by disclosing matching semantics the schema cannot express: names and common aliases are matched against a fixed catalogue, which tells the agent how to phrase the query.

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 (search/find) and resource (a fixed catalogue of local-LLM hardware and models), and explicitly names the trigger condition: the user names a GPU, Mac, mini PC or language model and you need its id. It is clearly distinguishable from get_model_specs, check_hardware_fit and 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 Guidelines5/5

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

Front-loads 'Use this first when...' and states the prerequisite (you need an id for the other tools), which routes the agent ahead of the spec/fit/compare siblings. It also gives explicit exclusions: no specs, no fit or speed, no web search.

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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