There's An AI For That

List feature models

list_feature_models

Read-only

List every AI model on one TAAFT feature, in the same order as the feature page. This is a complete listing, not a search: no query, no relevance ranking, nothing missing and nothing extra. Each model carries the full get_model fields. Preserve returned URLs exactly.

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": {
      "feature": "<feature>"
    },
    "name": "list_feature_models"
  }
}

Input Schema

Name	Required	Description
`limit`	No	Page size. Keep calling with next_offset until has_more is false to get the complete listing.
`offset`	No	Number of models to skip, in feature page order.
`feature`	Yes	Feature id, feature slug, or exact feature name as returned by list_features.

Output Schema

Name	Required	Description	Default
`limit`	Yes
`total`	Yes
`models`	Yes
`offset`	Yes
`feature`	Yes
`has_more`	Yes
`next_offset`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

AddedOct 21, 2026

TDQS

A4.3/5.0

Behavior4/5

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

Annotations already declare readOnly/openWorld=false/destructive=false, so safety is covered. The description adds genuinely useful behavioral context: results are ordered like the feature page, each model carries full get_model fields, and URLs must be preserved exactly. It does not, however, elaborate on response shape or failure modes beyond that.

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?

Three tight sentences, front-loaded with the core purpose and zero filler. 'Preserve returned URLs exactly' is a slightly odd instruction but still earns its place given the URL-fidelity concern.

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?

An output schema exists, so return-value explanation is not required, and the description notes the returned records hold full get_model fields. Combined with the 100% schema coverage, an agent has what it needs; only deeper failure/edge behavior is absent.

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% and the schema already explains pagination, offset ordering, and accepted feature identifier formats. The description reinforces completeness/ordering but adds no parameter syntax or meaning beyond the schema, so baseline 3 applies.

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 (List) and resource (AI models on a TAAFT feature) with scope ('every', 'complete listing'), and explicitly contrasts with the sibling search_models family by denying query/relevance ranking. An agent can distinguish it from get_model and search_models without opening a 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?

'This is a complete listing, not a search: no query, no relevance ranking' explicitly names when to use this over search alternatives, and the schema param notes reinforce the pagination loop. Nothing about the when-to-use decision 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.

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