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Recao

by app.recao

assemble_video

Stitch rendered scene clips into one ad-ready MP4 with burned-in subtitles and crossfades. Pass 1 to 6 clips in order, each with its mp4 url, its duration in seconds (3 to 12), and an optional subtitle line to burn over that clip. Every clip is scaled and padded to the chosen aspect (9:16 default 1080x1920, 1:1 1080x1080, or 16:9 1920x1080). Optional intro and outro title cards: pass intro/outro as { text, duration_s? } to bookend the ad with a centred title card (1 to 5 seconds, default 2) that crossfades into the video; cards do not count toward the 6-clip limit. Assembly is our compute, so it carries a small flat fee (about 0.12 EUR), booked only when the file is produced, against your trust cap. Runs in the background over up to a couple of minutes and returns a job_id: poll assemble_status with it to get the finished MP4 url. Feed it the clips from clips_status (map each to url, duration_s, subtitle). Paid plans only.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
clipsYes1 to 6 clips in playback order
introNoOptional intro title card shown before the clips.
outroNoOptional outro title card shown after the clips (e.g. a call to action).
aspectNoOutput aspect ratio: 9:16 (default, 1080x1920), 1:1 (1080x1080), or 16:9 (1920x1080).9:16

TDQS

A4.7/5.0
Behavior5/5

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

The description adds significant behavioral context beyond annotations, including that assembly carries a flat fee, runs in the background, returns a job_id for polling, and is non-idempotent. It aligns with annotations (readOnlyHint: false, idempotentHint: false) and provides specifics.

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?

The description is dense but every sentence adds necessary information. It is well-structured: starting with the core action, then input details, fees, async behavior, and links to related tools. No superfluous content.

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?

The description covers all aspects: input, constraints, fees, background processing, output as job_id, polling with assemble_status, and references to clips_status. Given the complexity of the tool, this is highly complete.

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?

With 100% schema coverage, the description still adds value by explaining constraints like clip limit 1-6, duration range 3-12, and the fact that intro/outro cards don't count toward the clip limit. This augments the schema descriptions effectively.

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's purpose: stitching rendered scene clips into an ad-ready MP4 with subtitles and crossfades. It distinguishes itself from siblings like assemble_status and clips_status by specifying that it consumes clips from clips_status and returns a job_id to poll with assemble_status.

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 provides clear context on when to use the tool: after having rendered clips, to create a final ad. It mentions it's for paid plans only and guides the user to feed clips from clips_status. While it doesn't explicitly state when not to use it, the 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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TDQS

A4.4/5.0
Disambiguation5/5

Each tool has a clear, distinct purpose. The status checkers (assemble_status, clips_status) are differentiated by the job type they monitor. Memory tools (index, note, recall) are distinct operations. Competitor ad tools (search, get ads, get details) and video generation pipeline (avatar, clips, assembly) are well-separated.

Naming Consistency5/5

All tools use consistent snake_case with a verb_noun or noun_verb pattern (e.g., assemble_video, get_competitor_ads, memory_index). No mixing of conventions, making the tool names predictable and easy to understand.

Tool Count5/5

With 17 tools, the server covers account management, memory, competitor research, and video generation without being overwhelming. The count is well-scoped for its functionality, neither too sparse nor too heavy.

Completeness4/5

The toolset covers the core workflows: account setup (sign_up, upgrade, whoami), memory (CRUD), competitor analysis (search, get ads, details), and video ad creation (avatar, clips, assembly). Minor gaps like lacking a delete memory tool are acceptable given the domain focus.

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