framesail

Update style references

update_style_references

Destructive

Replace a style's reference set — add or remove references without touching the analyzed fields.

FULL REPLACE: read the current list with get_style first and send every
entry you're keeping plus the changes. New entries are youtube (video
link, or a channel link/@handle) or text; new image/video FILES can only
be added via the multipart REST endpoint PATCH /styles/{id}, but existing
ones round-trip fine. Optional `contributes_to` scopes an entry to a
subset of the analyzed fields (art_style / narrative_style /
director_style); omit it for all three.

Saving does NOT re-run analysis: the response's `inputs_stale` flips true
and the analyzed fields keep their current text until the user asks for
analyze_style (which rewrites them from the new set).

Input Schema

TableJSON Schema

Name	Required	Description	Default
`inputs`	Yes	The COMPLETE reference list the style should have after this call, in display order: [{"input_type": "youtube" \| "text" \| "image" \| "video", "value": "<url or text>", "contributes_to": ["art_style", ...]?}]. Include every existing entry you are keeping (copy them from get_style verbatim — public URL values are fine); anything left out is removed
`style_id`	Yes	Style ID, as returned by create_style or list_styles

TDQS

A4.6/5.0

Behavior5/5

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

Beyond the destructiveHint=true annotation, the description reveals critical non-obvious behaviors: full-replace semantics (anything left out is removed), new entries restricted to youtube/text while files need the REST endpoint, and — most valuably — that saving does NOT re-run analysis, inputs_stale flips true, and analyzed fields keep their text until analyze_style is called. This is exactly the behavioral context an agent cannot infer from annotations or schema alone, and it contradicts nothing in the annotations.

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?

The description is long (~180 words) but every section earns its place: full-replace semantics, entry-type restrictions, contributes_to scoping, and the stale-analysis behavior are all operationally necessary for correct invocation. It is front-loaded with the core purpose and organized into clear paragraphs. Could be tightened slightly, but the density is justified by the tool's complexity.

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?

For a tool with no output schema and complex semantics, the description covers everything an agent needs: parameters are fully explained, response behavior (inputs_stale) is disclosed, destructive full-replace semantics are stated, and the alternative path for files is given. The order of operations (get_style → update → analyze_style) is specified. No material operational gap remains.

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% — both parameters are documented in the schema, so the baseline is 3. The description adds meaning beyond the schema by explaining that contributes_to scopes an entry to a subset of analyzed fields and can be omitted for all three, and by clarifying which input types are valid for new entries (youtube/text only). This meaningfully supplements the schema's structural example.

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 first sentence states a specific verb and resource: "Replace a style's reference set — add or remove references without touching the analyzed fields." This clearly distinguishes the tool from siblings like update_style_fields (which touches analyzed fields) and analyze_style (which rewrites them). The purpose is unambiguous and the resource scope is precise.

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 operational guidance: read the current list with get_style first, send every kept entry plus changes, and follow up with analyze_style when analysis should be re-run. It also explicitly excludes one case by directing new image/video files to the multipart REST endpoint. It does not explicitly name update_style_fields as the alternative for changing analyzed fields, though the contrast is implied by "without touching the analyzed fields."

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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