framesail

Get workflow status

get_workflow_status

Read-onlyIdempotent

Poll this between steps: returns active + recently-finished AI jobs (scope by project_id, or style_id for style analysis), plus per-segment- asset render statuses for projects. A step is done when its jobs reach status=complete (or error, with a user-readable message). NOTE: finished jobs drop out of jobs after ~30s — a short list does NOT mean work was lost; judge render batches by segment_assets statuses (or get_pipeline_progress), never by counting jobs. Prefer await_jobs over polling this in a tight loop.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`style_id`	No	Style ID to scope jobs to (style analysis); pass exactly one of project_id or style_id
`project_id`	No	Project ID to scope jobs to; pass exactly one of project_id or style_id

TDQS

A4.5/5.0

Behavior4/5

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

Annotations already declare readOnlyHint and destructiveHint. The description adds crucial behavioral context: finished jobs drop out after ~30s, a short list does not mean work lost, and how to correctly assess render batches. No contradictions.

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?

Four sentences, well-structured, with clear language and a notable warning in bold. No wasted words; every sentence adds value.

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 the tool's complexity (transient jobs, multiple scope options), the description covers usage, interpretation of results, and links to sibling tools. No output schema exists, but the description hints at return structure (jobs, segment_assets, error messages) adequately.

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% with descriptions for both parameters. The description adds minimal extra value (e.g., 'for style analysis' for style_id) but largely repeats what the schema already states. Baseline 3 is appropriate.

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 returns active and recently-finished AI jobs, scoped by project_id or style_id, plus per-segment-asset render statuses. It distinguishes itself from siblings like await_jobs and get_pipeline_progress.

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?

Explicitly advises polling between steps, recommends preferring await_jobs over tight polling, and warns against counting jobs to judge render batches. Provides clear when-to-use and alternative guidance.

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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