Skip to main content
Glama
wassermanproductions

unofficial-davinci-mcp

mix_plan

Plan audio mixes by setting dialogue normalization, music bed level, and ducking. Render a re-measured premix WAV upon confirmation.

Instructions

Plan a dialogue/music/sfx mix: dialogue normalisation gain, music bed level, and ducking windows; render a re-measured premix WAV on confirm.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sfxNo
rampNo
musicNo
confirmNo
dry_runNo
duck_dbNo
dialogueYesDialogue file(s) / track.
output_pathNo
music_bed_dbNoMusic bed level relative to dialogue LUFS.
dialogue_lufsNo
music_fade_inNo
music_fade_outNo
Behavior2/5

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

No annotations are present, so the description must carry the burden of behavioral disclosure. It indicates a non-destructive planning phase with a confirm trigger for rendering, but it does not mention whether it modifies project files, requires an open project, or what permissions are needed. The 're-measured' term hints at loudness analysis but is vague.

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 a single, front-loaded sentence that captures the core purpose quickly. It is concise with no wasted words. However, for a tool with 12 parameters, a structured bullet list might improve clarity without adding length.

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

Completeness2/5

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

Given the tool's complexity (12 parameters, no output schema, low coverage), the description leaves significant gaps. It does not explain return values, the relationship between confirm and dry_run, or the meaning of ramp parameter. The description is insufficient for an agent to use the tool correctly across all scenarios.

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

Parameters2/5

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

Schema coverage is only 17% (2 of 12 parameters have descriptions). The description adds some meaning by referencing dialogue normalization, music bed level, and ducking windows, but it does not explain most parameters (e.g., ramp, dry_run, output_path). With low coverage, the description should compensate more thoroughly.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it plans a mix involving dialogue, music, and SFX normalization, bed level, and ducking, with a render on confirm. It is specific and uses verbs like 'plan' and 'render'. However, it does not differentiate from sibling tools like 'cut_music' or 'measure_loudness', missing an opportunity to clarify its unique role.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description lacks any guidance on when to use this tool versus alternatives. It mentions a confirm step but does not explain prerequisites or scenarios where other tools might be preferred. No exclusions or context for usage are provided.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/wassermanproductions/unofficial-davinci-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server