framesail

Regenerate segment asset

regenerate_segment_asset

Regenerate a segment's primary image or video with optional overrides — the API equivalent of the editor's expert drawer. asset_type: "image" | "video" (for a video segment, "image" targets its start frame). Use a different model, override prompt sections (see get_section_template("segment_image")), or tweak settings (e.g. image quality), then re-run just this asset. Async — returns {job_id}.

Input Schema

TableJSON Schema

Name	Required	Description
`model`	No	Model ID to render with; empty uses the job's default (see list_models("segment_image") / list_models("segment_video"))
`settings`	No	Model-specific settings (e.g. image quality, video duration); valid keys come from the model's settings_schema in list_models
`asset_type`	Yes	"image" or "video"; for a video segment, "image" targets its start frame
`project_id`	Yes	Project ID, as returned by create_project or list_projects
`segment_number`	Yes	1-based segment number, as reported by get_segments
`editable_sections`	No	Per-call prompt section overrides, keyed by section name; see get_section_template("segment_image") or ("segment_video")

TDQS

A4.5/5.0

Behavior4/5

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

Annotations are non-committal (no readOnly/hints), but the description adds key behavioral details: async execution, returns {job_id}, and that it is the API equivalent of the editor's expert drawer. This goes beyond 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?

The description is concise (4 sentences) with no wasted words. It front-loads the main purpose, then clarifies key parameters and async behavior, making it easy to parse quickly.

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

Completeness5/5

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

Given no output schema, the description adequately explains the return value ({job_id}). It also references related tools for parameter details, making it self-contained for an agent. No gaps are apparent.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with descriptions, but the description adds context beyond the schema, such as clarifying that asset_type 'image' for a video segment targets the start frame, and listing the purpose of editable_sections and settings.

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 description clearly states the tool regenerates a segment's primary image or video with optional overrides, explicitly mentioning asset_type and async behavior. It distinguishes itself from siblings by focusing on regenerating individual assets.

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 explains when to use the tool (re-run just this asset with different model, overrides, settings) and references related tools like get_section_template and list_models. However, it does not explicitly state when not to use it or mention alternatives.

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.

Resources

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