PostNitro

Import Custom Carousel

postnitro_import_carousel

Create a carousel by importing your own slide content.

IMPORTANT: Call postnitro_get_import_template FIRST to see the exact slide structure and rules.

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

Strict rules (violations will cause errors):

First slide MUST be type "starting_slide" (exactly 1)
Middle slides MUST be type "body_slide" (at least 1)
Last slide MUST be type "ending_slide" (exactly 1)
Infographic columnCount must not exceed 3
When columnDisplay is "cycle", put ALL data in the FIRST columnData entry only
Setting layoutType "infographic" replaces the image field

Minimal example: slides: [ { type: "starting_slide", heading: "Welcome!", description: "Intro", cta_button: "Swipe →" }, { type: "body_slide", heading: "Key Point", description: "Details here" }, { type: "ending_slide", heading: "Thanks!", cta_button: "Learn More" } ]

Input Schema

TableJSON Schema

Name	Required	Description
`slides`	Yes	Array of slides
`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. '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

A4.8/5.0

Behavior5/5

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

Annotations already indicate non-readonly, non-destructive, non-idempotent behavior. The description discloses additional side effects: generateImages consumes AI-image quota, adds latency, and may fail gracefully. It also explains responseType options and their performance implications, providing full transparency without contradicting annotations.

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 lengthy but well-organized: a clear one-sentence purpose, an IMPORTANT callout, a numbered list of strict rules, and a concise minimal example. Every section earns its place given the complexity of the tool's input; it is not padded or redundant.

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?

Given the tool's complexity (6 parameters, nested objects, enums, optional configurations), the description covers all essential usage aspects: prerequisite call, structural rules, output format choices, image generation behavior, and fallback handling. The minimal example anchors the concept. Nothing critical is missing for an agent to use the tool correctly.

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 covers 100% of parameters with descriptions, so baseline is 3. The description adds value by illustrating the slides array structure with an example and highlighting key constraints (slide types, column count, cycle behavior), which helps agents understand how to populate nested fields correctly. This goes slightly beyond the schema alone.

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?

States a specific verb ('Create') and resource ('carousel'), and clarifies the method ('importing your own slide content'), which distinguishes it from generation tools like postnitro_generate_carousel. The purpose is immediately clear.

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?

Provides explicit 'IMPORTANT' instruction to call postnitro_get_import_template first, enumerates strict structural rules, includes a minimal example, and notes optional parameters when defaults are saved. This gives the agent practical, unambiguous guidance on how and when to use the tool.

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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