analyze_styleRe-run style analysis (after changing a style's inputs). Async — await_jobs(style_id=...) until the style_analysis job completes.
| Name | Required | Description | Default |
|---|---|---|---|
| style_id | Yes | ID of the style to re-analyze, from create_style or list_styles |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations declare destructiveHint=true, and description adds async behavior and job type detail. No contradiction. Adds value beyond annotations by explaining awaiting pattern.
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 concise sentences, no redundancy. All information is front-loaded and relevant.
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 single-parameter async analysis tool, description provides enough context. Could mention output implications, but no output schema exists. Adequate.
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 coverage is 100% with one parameter (style_id) described. Description does not add extra parameter meaning beyond schema, but mentions it indirectly. Baseline 3 is appropriate.
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?
Description clearly states the tool re-runs style analysis after changing inputs, distinguishing it from related tools like 'create_style' or 'get_style'. The verb 're-run' and resource 'style analysis' are explicit.
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?
Explicitly says when to use (after changing style inputs) and how to handle the async job (await_jobs). Lacks explicit 'when not to use', but context is sufficient.
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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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.
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.
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.
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.