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Glama

Create clip job (spends credits)

create_clip_job
Destructive

Start an AI clipping job to turn a long podcast, stream, interview, or vlog into captioned vertical Shorts, Reels, or TikTok clips with hooks and optional music and B-roll.

Instructions

Starts AI clipping of a long video into vertical 9:16 short clips with burned-in captions and a hook title, plus optional cold-open hooks, music and B-roll. Spends credits: 1 per started minute of the processed range (minimum 1), charged when the job is created. One job processes at most 30 minutes of video; split longer videos with start_sec and end_sec. Jobs cannot be cancelled.

Before calling, run quote_clip_job, show the price to the user and get explicit agreement. max_credits is required; the call is refused without charging if the price is higher. Some clients also ask the user to confirm. If the same request was already submitted with this API key in the last 10 minutes, that job is returned instead of charging again.

Use for turning a podcast, stream, interview, lecture or vlog into Shorts, Reels or TikTok clips. Not for: transcripts only (create_transcription), changing the look of an existing job (recut_clip_job), or re-running a failed job for free (retry_clip_job).

Key parameters: source_url (public link; local files are not supported), start_sec and end_sec to process only part of the video, video_mode (podcast for talking heads), clip_length, caption_style, language (omit to auto-detect) and prompt to steer which moments are picked. Call get_options_guide to explain option values.

Returns job_id right away with status queued and the credits charged. Processing takes a few minutes for short videos and up to tens of minutes for a full 30-minute range; clips appear one by one. Next, call get_clip_job with wait_sec=50 until the status is completed or failed. If processing fails, credits are not returned automatically: retry_clip_job re-runs the job for free, or the user can contact support@mnogo-reels.ru with the job_id. On the free plan clip files are kept for 30 days.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
outroNoEnding added to every clip: default branded outro, soon/like short Russian text cards, custom = your saved outro image.none
emojisNoAdd emojis to captions where they fit.
promptNoWhat kind of moments to pick, in plain words, e.g. 'funny moments about money'.
end_secNoEnd of the part to process, in seconds. Default: the end of the video (its length is looked up automatically when the platform reports it). At most 30 minutes after start_sec.
framingNoauto = smart crop; full_frame = keep the whole wide frame on a blurred background; portrait_only = always crop to the subject.auto
languageNoTwo-letter language code of the speech, e.g. en or ru. Omit to auto-detect. Never pass 'auto'.
add_musicNoAdd quiet background music from the MnogoReels library.
cold_openNoStart each clip with its strongest moment as a teaser, and also produce standalone hook clips and a looped teaser.
smart_cutNoRemove pauses and filler words inside clips. Works only with video_mode podcast (general-beta always does it); ignored for other modes.
start_secNoStart of the part to process, in seconds from the beginning. Default 0.
hook_styleNoStyle of the hook title shown at the start of each clip; none hides it.default
source_urlYesPublic link to the video: YouTube, VK Video, RuTube, Dzen, Twitch, Yandex Disk file, Google Drive file, or a direct .mp4/.mov/.mkv/.webm link. Local files are not supported.
video_modeNoHow the video was filmed: general, podcast (talking heads), general-beta (SmartCut: removes pauses and filler words), stream (screen + webcam), vlog, fitness.general
clip_lengthNoTarget length of each clip. auto lets the AI decide; 30_60s suits Shorts, Reels and TikTok.30_60s
max_creditsYesSpending limit for this call in credits. Required. Use the credits value from quote_clip_job after the user agreed; the call is refused without charging if the price (or its upper bound) is higher.
agentic_modeNoExtra AI review pass that refines where clips start and end. Slower.
caption_styleNoBurned-in caption style; empty means no captions. Call get_options_guide to see what each style looks like.karaoke
allow_duplicateNoSet true only if the user explicitly wants a second identical job within 10 minutes (it is charged again).
story_arc_styleNoBuild each clip as a story (cold open, main part, payoff). Any value except none also turns on cold_open.none
b_roll_ai_imagesNoInsert AI-generated illustrative images as B-roll.
keep_whole_videoNoDo not cut into clips: render the whole video once with captions, music and B-roll. Use only for videos that are already vertical and short.
b_roll_stock_videoNoInsert relevant stock video B-roll.
b_roll_motion_graphicsNoInsert AI motion-graphics B-roll (animated titles, charts, diagrams).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
job_idYes
statusYes
creditsYesCredits charged for this job (same formula as the API).
end_secYes
next_stepYese.g. "Call get_clip_job with job_id and wait_sec=50."
start_secYes
source_urlYes
deduplicatedYesTrue when an identical request from the last 10 minutes was returned instead of creating a new job (nothing was charged now).

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A4.9/5.0
Behavior5/5

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

Goes well past the annotations: exact pricing model (1 credit per started minute, minimum 1, charged at creation), the 30-minute-per-job cap with start_sec/end_sec as the split mechanism, no-cancellation, the max_credits refusal behavior, the dedupe-within-10-minutes rule, the async lifecycle (job_id returned with status queued, poll get_clip_job with wait_sec=50), and the no-automatic-refund failure path. The annotation set (destructive/non-idempotent/openWorld) is consistent with this, and the dedupe nuance is stated as a bounded exception rather than a contradiction.

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?

Long, but proportionate to a 23-parameter, credit-spending, non-cancellable job creator. It is front-loaded with the spend warning, then preflight, then routing, then key parameters, then return/polling behavior — each block is distinct and no sentence is filler.

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?

Covers everything an agent needs to call this safely: required preflight, cost, hard caps, dedupe rules, required parameters, the follow-up polling call with a concrete wait_sec, and failure/retention handling. An output schema exists, so the description is not obliged to explain the response body, and it correctly limits itself to noting job_id/status.

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%, so baseline is 3. The description adds real orientation on top: it flags which parameters matter (source_url, start_sec/end_sec, video_mode, clip_length, caption_style, language, prompt), states that local files are unsupported, explains the 30-minute range constraint that motivates splitting, and routes enum-heavy parameters to get_options_guide. It stops short of describing every one of the 23 options, which is defensible given the schema.

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?

Opens with a specific verb+resource ('Starts AI clipping of a long video into vertical 9:16 short clips') and enumerates the concrete output features (captions, hook title, cold-open, music, B-roll). An agent can distinguish it from create_transcription, recut_clip_job and retry_clip_job immediately, since those are named as the wrong tools for adjacent needs.

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?

Explicit when-to-use (podcast, stream, interview, lecture, vlog → Shorts/Reels/TikTok), explicit when-not with the correct alternative for each case (create_transcription, recut_clip_job, retry_clip_job), a mandatory preflight ('run quote_clip_job, show the price... get explicit agreement'), and the 10-minute duplicate window with allow_duplicate as the escape hatch.

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