PostNitro

Import Video Post

postnitro_import_video

Create a video post (postType VIDEO) from your own scene content. Returns an embedPostId to track progress.

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. Infographic layouts work as they do on carousel slides.

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

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

Use postnitro_check_status to monitor, then postnitro_get_output to retrieve. Or use postnitro_import_video_and_wait for one step.

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
`status`	Yes	Initial job status ('PENDING')
`message`	No	Message returned by the PostNitro API
`success`	Yes	True when the job was accepted
`nextStep`	Yes	The suggested follow-up call
`warnings`	No	Non-fatal issues worth surfacing to the user (e.g. AI image generation did not complete)
`embedPostId`	Yes	Generation-job ID — pass to postnitro_check_status, then postnitro_get_output
`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 that the tool returns an embedPostId, tracks progress, and supports DESIGN vs MP4 output, which goes beyond the annotations. It does not contradict the readOnly/destructive/idempotent hints, and while it doesn't mention credit consumption, the async tracking info is sufficient.

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 well-structured and each sentence adds value, but it is somewhat long with multiple paragraphs. It could be slightly more condensed without losing important details, yet it remains efficient and readable.

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 the core action, output types, defaults, monitoring workflow, and alternative one-step tool. It gives an agent everything needed to decide when and how to use this tool correctly, including the slide-array relationship to carousels and infographic layouts.

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?

The input schema already covers all parameter descriptions at 100% coverage, so the baseline is 3. The description adds a few high-level reminders (e.g., videoSettings required for MP4, defaults optional) but does not provide substantial new parameter semantics beyond what the schema already states.

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 creates a video post from user-provided scene content, using a specific verb and resource. It distinguishes itself by emphasizing 'own scene content' (import rather than generate) and explains the slide structure clearly.

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?

Explicitly tells the agent to use postnitro_check_status and postnitro_get_output for monitoring/retrieval, and points to postnitro_import_video_and_wait as a one-step alternative. It also notes that templateId/brandId/responseType can be omitted if defaults are saved, giving clear when-to-use guidance.

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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