PostNitro

Generate Carousel and Wait for Output

postnitro_generate_and_wait

Convenience tool: generates a carousel with AI, polls until completion, and returns the final output. May take 30-180 seconds.

For a single-image post instead of a carousel, use postnitro_generate_image_and_wait.

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

Input Schema

TableJSON Schema

Name	Required	Description
`brandId`	No	Brand ID (optional if saved via postnitro_set_defaults)
`presetId`	No	AI preset 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
`aiGeneration`	Yes
`responseType`	No	Output format. 'DESIGN' (default) creates the design with no rendered file — fastest, and enough for scheduling/editing. Use 'PDF' or 'PNG' when you need a rendered file back. Optional if saved via postnitro_set_defaults.
`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

A4.2/5.0

Behavior3/5

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

The description discloses that the tool polls until completion, returns the final output, and may take up to 180 seconds. It does not mention side effects such as credit consumption or that repeated calls may create multiple generations, but annotations do not contradict the described behavior.

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 includes several helpful usage notes, but it is somewhat long and repeats the 'optional if saved via defaults' point multiple times. Overall it remains focused and readable.

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 complexity of the nested parameters, the description covers key operational details such as polling, response types, image generation behavior, and failure best-effort outcomes. It does not describe the exact return shape, but the context indicates an output schema exists, so this is not a critical gap.

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 description adds significant value beyond the schema, especially for generateImages.context by instructing the agent to author the brief itself and not ask the user. It also clarifies responseType 'DESIGN' behavior, image strategy and placement choices, and the paid-plan/quota/latency implications of image generation.

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?

Description clearly states the tool generates a carousel with AI, polls until completion, and returns the final output. It explicitly distinguishes itself from the single-image sibling tool, making its purpose and scope unambiguous.

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 useful usage context: it is a convenience tool that waits and returns output, may take 30-180 seconds, and notes where optional defaults apply. It points to an alternative for single-image posts, though it does not explicitly contrast with all related generation/scheduling tools.

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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