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list_models

Retrieve a paginated list of served AI models, using a limit per page and a cursor to continue paging and inspect available models.

Instructions

List served AI models (kind=model workloads), bounded to limit per page.

    Returns a cursor-paginated page; pass ``cursor`` from the previous page
    to continue. A newly served model shows ``building`` until its off-tick
    pull completes; lifecycle actions go through the service tools.

Input Schema

NameRequiredDescriptionDefault
limitNoModels per page; a value outside 1–200 is clamped into it (0 becomes 1, 10000 becomes 200). Omit for 50.
cursorNoOpaque paging cursor from the previous page's response; omit for page 1.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.8/5.0
Behavior4/5

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

No annotations are present, so the description carries the full burden and does reasonably well: it discloses cursor pagination, the fact that newly served models appear as 'building' until the off-tick pull completes, and that lifecycle operations live elsewhere. It still says nothing about permissions, rate limits, or result ordering.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loaded with the resource and scope, then pagination, then the lifecycle caveat. Slight redundancy with the schema on limit/cursor, but no filler sentences.

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

Completeness3/5

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

With no output schema and no annotations, the description is the only source of truth, and it never says what a model record contains (name, id, status fields) or what ordering to expect. Pagination and the 'building' state are covered, but the return shape is left undefined.

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 coverage is 100%, so both parameters are already fully documented, including clamping behavior and the default of 50. The description only restates 'bounded to limit per page' and 'pass cursor from the previous page', adding no format or edge-case detail beyond the schema. Baseline 3 applies.

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?

Uses a specific verb+resource ('List served AI models') and narrows scope with '(kind=model workloads)', which separates it from cluster/host listings. It never names the closest-looking sibling, list_ai_provider_models or list_services, so the boundary is inferred rather than stated.

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?

'lifecycle actions go through the service tools' steers the agent away from using this tool for mutation and points at the service family. It gives no explicit condition for choosing between this and list_ai_provider_models or list_services, so a small inference remains.

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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