PostNitro

Generate Carousel with AI

postnitro_generate_carousel

Generate a carousel post using PostNitro's AI engine. Returns an embedPostId to track progress.

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

templateId, brandId, presetId, and responseType are optional if you've saved defaults via postnitro_set_defaults. Otherwise provide them here (use the list tools to find valid IDs).

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

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
`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 to track progress, implying asynchronous execution. It also references checking status and retrieving output, which further signals non-blocking behavior. However, it doesn't explicitly mention potential side effects like consuming credits or that it creates a persistent resource, but this is not required given the annotations.

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?

The description is concise, with four short sentences. It front-loads the core action, then provides the alternative, then parameter guidance, then workflow. No wordy or redundant content. Ideal length.

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 tool has a complex schema with nested objects (e.g., generateImages), the description doesn't mention those details, but that's appropriate since the schema carries them. The description does cover the essential context: what it generates, how to track it, and when to use an alternative. It lacks a note about the optional image generation feature, but that's discoverable via the schema, so completeness is adequate.

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 schema provides descriptions for all parameters (coverage 86%), and the tool description does not add significant meaning beyond what's already in the schema. It mentions that some parameters are optional if defaults are saved, but that's already stated in the parameter descriptions. Thus, the description adds no extra semantic value.

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's primary function: 'Generate a carousel post using PostNitro's AI engine.' It also distinguishes from a sibling by noting 'For a single-image post instead of a carousel, use postnitro_generate_image.' This leaves no ambiguity about what the tool does.

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?

It explicitly provides guidance on when to use this tool vs. alternatives: 'For a single-image post instead of a carousel, use postnitro_generate_image.' It also explains the follow-up workflow: 'Use postnitro_check_status to monitor, then postnitro_get_output to retrieve. Or use postnitro_generate_and_wait for one step.' Additionally, it clarifies optional parameters: 'templateId, brandId, presetId, and responseType are optional if you've saved defaults via postnitro_set_defaults.'

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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