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flozonn

Mirelo MCP Server

by flozonn

mirelo_inpaint_audio_with_video_submit

Replace a specific audio segment within a video by conditioning on the video content. Submit audio and video sources along with a time segment to generate replacement audio, receiving a job ID to track progress.

Instructions

Submit an asynchronous Inpaint_Audio (with-video) job that replaces a bounded audio segment conditioned by a video, returning a job identifier to poll with mirelo_job_status. Requires audio and video Input_Sources and a segment. Model v1.6 only.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
audioYesThe audio Input_Source to inpaint: either { type: "url", audio_url } or { type: "asset", asset_id }.
videoYesThe conditioning video Input_Source: either { type: "url", video_url } or { type: "asset", asset_id }.
promptNoOptional text prompt guiding the replacement audio.
segmentYesThe bounded audio span to replace.
num_samplesNoNumber of samples to generate; integer >= 1. Defaults to 1.
model_versionNoModel version. Inpaint_Audio is v1.6-only; defaults to "v1.6".
Behavior3/5

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

No annotations provided, so description carries full burden. It discloses async nature, model version constraint, and required inputs. However, it does not discuss job lifecycle details (e.g., typical duration, cancellation, failure modes) or prerequisites (e.g., asset uploads). These gaps limit transparency despite adequate core disclosure.

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?

Two sentences, no redundancy, critical information front-loaded: purpose, async behavior, required inputs, model version. Every word contributes value.

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

Completeness4/5

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

For a submit tool with no output schema, the description explains the return value (job identifier) and how to poll it. It covers the main use case but could be slightly more complete by mentioning optional parameters (prompt, num_samples) in context, though they are documented in schema.

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 each parameter. The description adds a high-level summary ('requires audio, video, segment') and model version constraint, but does not provide meaning beyond what's in the schema. 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?

Description clearly states the verb 'Submit', the resource 'Inpaint_Audio (with-video) job', and the action 'replaces a bounded audio segment conditioned by a video'. It distinguishes from siblings by noting it's asynchronous and returns a job identifier, contrasting with synchronous generate variants.

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?

Describes when to use (asynchronous job for audio inpainting with video conditioning) and explicitly notes model restriction (v1.6 only) and polling requirement via mirelo_job_status. Does not explicitly exclude alternatives or compare to preflight/generate siblings, but context is clear enough.

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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