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by ai.weftly

clips_horizontal

Batch-cut a set of your own chosen horizontal 16:9 clips from a prior find_clips job in one purchase, delivered as SEPARATE files. Two-call flow: (1) call with source_job_id (a find_clips job id) and clips (1-5 objects {start, end, title?} in source seconds — your own picks from that job's clip-candidates output; nothing is auto-selected) to receive {job_id, payment_challenge}; (2) pay by credit card via the returned payment_url or Tempo USDC via mppx, then call again with job_id + payment_credential to start processing. Poll get_job_status(job_id); outputs are roles clip-1-video through clip-N-video (one .mp4 per requested clip, source framing and resolution preserved) plus clips-manifest (JSON) recording each clip's timing, output role, and any per-clip failure — a batch delivering fewer than N clips is not refunded, only one delivering zero is. Price: N × $0.50 per clip, charged once for the whole batch (Stripe quantity) rather than once per clip — see /.well-known/mpp.json for the Tempo USDC rate. No profile or subject parameters: horizontal keeps the source framing, so there is no crop to aim. Choose deliberately between this and extract_clip: extract_clip with several segments concatenates them into ONE highlight reel at the single-clip price, while this cuts N SEPARATE files at N × that price. Use this when each moment stands alone; use extract_clip when you want one file, need per-clip transcripts, or need more than 5 moments from one source. Use clips_vertical instead for 9:16 social output. Source must still be in storage — check expires_at on the find_clips parent via get_job_status. Retrying with job_id alone recovers the current state.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
clipsNoYour own chosen clips (1-5) from source_job_id's clip-candidates output — nothing is auto-selected. Each is cut as a SEPARATE horizontal file; the batch is charged N × the per-clip price as one payment covering the whole set, not one charge per clip. Total duration across all clips is capped. Required on the first call.
job_idNoJob ID returned from a previous clips_horizontal call. Include along with payment_credential to confirm payment and trigger processing. Also include alone to recover the current state.
source_job_idNoJob ID of a prior find_clips job. Possessing this id is the capability — clips_horizontal is not session-bound, so a user can come back from a different session within the parent's TTL and still cut. Required on the first call.
payment_credentialNoMPP payment credential (full Authorization header value, e.g. "Payment eyJ..."). clips_horizontal accepts Tempo USDC and Stripe SPT — see the challenge's WWW-Authenticate header or /.well-known/mpp.json for the supported methods. Include with job_id after paying the challenge to start processing.

TDQS

A4.9/5.0
Behavior5/5

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

With no annotations, the description carries the full burden of behavioral disclosure. It thoroughly covers the two-call payment flow, refund policy (no refund for fewer than N clips, only for zero), pricing (N × $0.50), output roles, retry semantics, and source TTL dependency. It also explicitly notes the absence of crop parameters, preventing incorrect calls.

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 lengthy but front-loaded with the core purpose and two-call flow. Every section serves a distinct need: flow, outputs, pricing, selection guidance, and error handling. Some redundancy exists (e.g., repeated 'SEPARATE files' emphasis), but the density is justified by the tool's complexity and the absence of annotations.

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 is exhaustive for a complex transactional tool. It covers the full lifecycle: prerequisites (prior find_clips job), call sequence, payment methods, output specification (roles clip-1-video through clip-N-video, manifest), failure handling, pricing model, and retry recovery. No critical information is missing given no output schema exists.

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

Parameters5/5

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

While schema coverage is 100%, the description adds significant meaning beyond the field names. It explains that `clips` are caller-chosen and nothing is auto-selected, that `source_job_id` acts as a capability token, and that `job_id` alone recovers state. It also clarifies that the batch charge is one Stripe payment for N clips, not per clip—critical for pricing understanding.

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 opens with a precise verb+resource: 'Batch-cut a set of your own chosen horizontal 16:9 clips from a prior find_clips job in one purchase, delivered as SEPARATE files.' This clearly distinguishes it from siblings like extract_clip (single file) and clips_vertical (format). The purpose is unambiguous.

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

Usage Guidelines5/5

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

The description gives explicit when-to-use instructions: 'Use this when each moment stands alone; use extract_clip when you want one file, need per-clip transcripts, or need more than 5 moments.' It also mentions clips_vertical for 9:16 output, and details the two-call flow and retry behavior, leaving no doubt about invocation context.

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
Disambiguation4/5

Most tools target distinct tasks (transcribe vs. summarize vs. find_clips, extract_clip vs. extract_vertical_clip, create vs. trigger YouTube). Some overlap exists because summarize and find_clips both produce transcripts, and publish_to_youtube and trigger_youtube_publish are sequential steps that could be confused, but descriptions clarify the boundaries well.

Naming Consistency4/5

The majority follow a verb_noun pattern (complete_upload, extract_clip, find_clips, get_job_status), with a few deviations like the bare verbs 'summarize' and 'transcribe', and 'publish_to_youtube' using a preposition. The pattern is still predictable and readable overall.

Tool Count5/5

11 tools is a well-scoped number for a video/audio processing service covering transcription, summarization, clip extraction, YouTube publishing, and payment testing. Each tool has a clear place, and the count is within the ideal range.

Completeness5/5

The tool surface covers the full lifecycle: job creation (transcribe/summarize/find_clips), payment (mpp_smoke_test, payment challenge flows), upload (complete_upload), status polling (get_job_status), clip extraction (two variants), and YouTube publishing (create/publish/status). No critical gaps are apparent for the stated domain.

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