PostNitro

Import Video Post and Wait for Output

postnitro_import_video_and_wait

Convenience tool: creates a video post (postType VIDEO) from your own scenes, polls until completion, and returns the final output. Rendering a video takes longer than a carousel — typically 15-45 seconds when responseType is 'MP4', and longer for designs with animations or GIFs (which use the enhanced renderer).

Scenes use the SAME slide array as a carousel: exactly 1 starting_slide, at least 1 body_slide, exactly 1 ending_slide — each slide becomes a scene.

Output: 'DESIGN' (default) creates the design without rendering; 'MP4' renders the video file and requires videoSettings.

templateId, brandId, and responseType are optional if you've saved defaults via postnitro_set_defaults.

Input Schema

TableJSON Schema

Name	Required	Description
`slides`	Yes	The scenes, as an array of 3+ typed slides (exactly 1 starting_slide first, 1+ body_slide, exactly 1 ending_slide last).
`brandId`	No	Brand ID (optional if saved via postnitro_set_defaults)
`templateId`	No	Template ID (optional if saved via postnitro_set_defaults)
`requestorId`	No	Optional custom tracking ID
`responseType`	No	Output format for a video. 'DESIGN' (default) creates the design without rendering — fastest, and enough for scheduling or finishing in the editor. 'MP4' renders the video file and requires videoSettings. A video cannot be rendered as PDF or PNG.
`videoSettings`	No	Video render settings. REQUIRED when responseType is 'MP4' (a render needs a duration); optional for 'DESIGN'. When this video is later scheduled as a reel, the API reuses these settings automatically.
`generateImages`	No	Optional AI image generation — include this object to enable it, omit it for no images. When included, `context` is required and you (the agent) must author it from the post. Best-effort: the post still COMPLETES if images fail or aren't permitted (free plan / exhausted AI-image quota); check the GENERATE_IMAGES step via postnitro_check_status. Requires a paid plan, consumes the org's AI-image quota (separate from post credits), and adds latency.

Output Schema

TableJSON Schema

Name	Required	Description
`data`	No	Public URL of the rendered file — a single URL for PDF and MP4, an array of them for PNG (one per page). Omitted for responseType 'DESIGN'.
`name`	No	Design name
`status`	Yes	Job status, e.g. 'COMPLETED'
`success`	Yes	True when the post finished processing
`designId`	No	Design ID — the value to pass as designId when scheduling. Absent only when it could not be resolved from the output.
`mimeType`	No	MIME type of the rendered file. Omitted for responseType 'DESIGN' (no file is rendered).
`warnings`	No	Non-fatal issues worth surfacing to the user (e.g. AI image generation did not complete)
`editorUrl`	Yes	Deep link that opens the design in the PostNitro editor; null when it cannot be resolved
`outputType`	No	Rendered output type: 'pdf', 'png', or 'mp4'. Omitted for responseType 'DESIGN'.
`aspectRatio`	No	Design aspect ratio, e.g. '4:5'
`creditsUsed`	No	Credits consumed by this post
`embedPostId`	Yes	The generation-job ID
`responseType`	Yes	Output format of the post: 'PDF', 'PNG', 'DESIGN', or 'MP4' (video posts)
`usedDefaults`	Yes	The values actually applied — explicit arguments, saved defaults, or an auto-selected sole candidate

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

First observedSep 7, 2026

TDQS

A4.4/5.0

Behavior4/5

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

The description discloses important behaviors such as polling until completion, rendering latency, the need for videoSettings when MP4 is requested, and reliance on saved defaults for optional parameters. It does not exhaustively cover failure or partial-completion behavior, but the main behavioral contract is clear.

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 organized in clear paragraphs and front-loads the core purpose. It repeats a little of the schema information, but the extra context is relevant and not overly verbose.

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?

Given the nested objects and multiple optional settings, the description provides enough high-level context to understand the tool's role and key constraints. It relies on the schema for detailed nested structures, which is appropriate.

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?

The schema already has high coverage, and the description adds extra meaning by clarifying scene/slide equivalence to carousel slides, the exact slide type counts, output modes, and how saved defaults affect optional parameters. This helps the agent understand the data model and requirements.

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 that the tool creates a video post from user-provided scenes, polls until completion, and returns the final output. It also distinguishes this from carousel generation by explicitly mentioning postType VIDEO and the scene/slide structure.

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 gives useful context on when to use this tool: it is a convenience tool that waits for completion, describes the expected rendering time, and explains the difference between DESIGN and MP4 outputs. It does not explicitly name alternative non-waiting tools, but the usage 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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TDQS

A4/5.0

Disambiguation4/5

Most tools are clearly separated by verb and noun (generate_carousel vs generate_image vs generate_video, import_* vs generate_*). The generic convenience names like generate_and_wait and import_and_wait are slightly ambiguous, but their descriptions and the _wait/_schedule suffixes make them distinguishable.

Naming Consistency4/5

All tools share the postnitro_ prefix and snake_case verb-first convention. A few compound names (generate_and_schedule, import_and_wait) break the strict verb_noun pattern, but the convention is otherwise consistent and predictable.

Tool Count2/5

With 35 tools, the surface exceeds the rubric's 25+ 'too many' threshold. Many entries are convenience wrappers (_wait, _and_schedule) around the same core operations and could be consolidated to reduce overload.

Completeness4/5

The domain is well covered: generation, import, output retrieval, scheduling, brands, social accounts, audio, templates, presets, and defaults. Minor gaps remain (no delete_brand, no upload/connect for audio/social accounts), but those are largely external or non-critical.

Resources

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