Retrieve and filter inventoried AI models by name, severity, supplier, or library. Use cursor-based paging to browse results beyond the 100-item limit.
List inventoried AI models. Filters: name, severity, supplier, library.
page_size is capped at 100; page with the returned cursor for more.
| Name | Required | Description | Default |
|---|---|---|---|
| name | No | ||
| cursor | No | ||
| library | No | ||
| severity | No | ||
| supplier | No | ||
| page_size | No |
Changes observed during successful MCP inspections.
v0.2.2Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden but only discloses pagination behavior (page_size capped at 100, cursor returned for continuation). It says nothing about read-only nature, auth requirements, or result shape, though for a read-list tool the omissions are moderate.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two short, front-loaded sentences: purpose and filters first, pagination constraint second. No filler; each sentence carries weight, though a fragment-style filter list is slightly terse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a 6-param, no-annotation, no-output-schema read tool, the description covers purpose, filters, and pagination but omits value formats for enum-like filters and any indication of what a returned model contains. Adequate but with clear gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0% across 6 params, so the description must compensate. It names all filter params (name, severity, supplier, library) and explains page_size/cursor, but adds no value semantics such as accepted severity values, matching behavior, or the page_size default.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
States a specific verb (List) and resource (inventoried AI models), and naming convention distinguishes it from the sibling get_model for single-model retrieval. It doesn't explicitly say it is the bulk/listing counterpart, so it falls just short of full sibling differentiation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The listing intent and available filters imply when to reach for it over get_model, but there is no explicit when/when-not guidance or routing to get_model for a single record. Usage is left to inference.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.