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kernel_models_catalog

Lists LLM models discovered per provider by the discovery cron, with optional filters for provider slug and status and timestamps for when each model was first and last seen.

Instructions

List LLM models discovered per provider by the model-discovery cron. Filter by provider slug and/or status (available | gone). Shows when each model was first/last seen.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "kernel_models_catalog"
  }
}

Input Schema

NameRequiredDescriptionDefault
slugNoProvider slug, e.g. minimax, claude, openai
statusNoFilter by status

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.5.1

TDQS

A3.6/5.0
Behavior3/5

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

No annotations are supplied, so the description carries the full burden; 'List' plus 'discovered by cron' implies a read-only, system-populated listing, and the 'available | gone' status hints that stale models persist rather than disappear. It does not state whether results are paginated, whether an empty result is possible, or what ordering is used.

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 short sentences, front-loaded with the action and resource, then filters, then return content. Every sentence adds information with no filler.

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

Completeness4/5

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

For a two-parameter, zero-required read tool with a fully documented schema and no output schema, the definition covers what it lists, how to filter, and what timestamps come back. Only minor gaps remain, such as ordering and pagination behavior.

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 both parameters are already documented, including the status enum and slug example. The description restates the filter combination ('and/or') and the status vocabulary but adds no format or matching semantics beyond the schema.

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?

The description states a specific verb and resource ('List LLM models discovered per provider') and adds provenance ('by the model-discovery cron'), which separates it from the config/marketplace catalog siblings. It does not explicitly name a sibling it replaces, so it stops short of a 5.

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?

It states the available filters ('provider slug and/or status') and the two status values, which implies how to narrow results, but gives no explicit when-to-use versus when-not guidance or reference to an alternative tool for a different view of models.

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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