framesail

List models

list_models

Read-onlyIdempotent

List the models allowed for a generation job, with display names, credit estimates, and each model's settings_schema — the valid keys for that tool's settings param (e.g. image quality/orientation, video duration). When model is omitted the server picks: the account's saved expert-drawer choice for that job/scope if one exists, else the first entry here. Voice models carry a provider field — a voice_block model must match the project's voice_tts_provider or generate_voiceover rejects it.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`job`	Yes	Generation job whose allowed models to list, e.g. "script", "storyboard", "segment_image", "segment_video", "voice_block"

TDQS

A4.2/5.0

Behavior4/5

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

Annotations already establish read-only, idempotent, and non-destructive behavior. The description adds genuine behavioral context beyond annotations: server-side model selection fallback, the meaning of settings_schema, and the provider matching requirement for voice_block models. No contradiction with annotations.

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 dense sentences, front-loaded with the main purpose, followed by two high-value clarifications about fallback behavior and voice model constraints. Every sentence earns its place and there is no redundant or filler content.

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 one-parameter read-only list tool with no output schema, the description covers return fields, fallback behavior, and a critical voice-model constraint. Minor gaps such as pagination or ordering are unlikely to matter here, and the description carries the burden well in the absence of an output schema.

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?

The input schema already fully documents the single job parameter with examples, and schema coverage is 100%. The description references job/scope but adds no new syntax or constraints for the parameter itself; the extra detail about settings_schema and provider applies to returned fields, not parameter usage.

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?

The opening sentence names a precise verb and resource: list the allowed models for a generation job, and specifies the returned fields (display names, credit estimates, settings_schema). This clearly distinguishes it from sibling list tools by tying it to generation jobs and model settings.

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?

The description gives concrete usage context: what happens when model is omitted, the fallback to the account's saved expert-drawer choice, and the voice model provider constraint relevant to generate_voiceover. It does not explicitly name alternatives or state when not to use this tool, so it stops short of a 5.

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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TDQS

A3.6/5.0

Disambiguation3/5

Several tool families overlap in purpose, such as await_jobs/get_workflow_status/get_pipeline_progress, update_segment_content/update_segment_prompts, director_note/project_director_note, and scan_script/rescan_voice_blocks. The descriptions do a good job distinguishing them, but an agent must read carefully to avoid misselection, and there are more than a couple of confusable pairs.

Naming Consistency4/5

The set overwhelmingly follows a verb_noun snake_case convention with clear prefixes like get_, list_, set_, update_, create_, and delete_. Minor exceptions such as director_note, project_director_note, browse_audio_library, and whoami keep it from being perfectly consistent.

Tool Count1/5

At 72 tools, this is far beyond the 50+ extreme range and creates a heavy navigation burden for an agent. Even though the pipeline is complex, this many tools is not well-scoped for an MCP surface.

Completeness4/5

The surface covers the full script-to-export pipeline: styles, assets, voices, storyboards, segments, scenes, and rendering all have substantial lifecycle support. Some gaps exist—no delete_channel, delete_segment, delete_voice_block, or delete_provider_key—but most missing operations can be worked around through existing tools.

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