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aaddyy_caption_generator

Generate image captions and social media posts in multiple tones, languages, and platforms. Create AI prompts or hashtag sets from any image for content creators.

Instructions

Generate creative captions, social media posts, and AI prompts from images using advanced AI analysis (~2 credits)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
toneNotrendy
imageUrlYes
languageNoenglish
platformNoinstagram
nsfwFilterNo
outputTypeNoshort_caption
captionFocusNoemotion
captionLengthNomedium
includeEmojisNo
customKeywordsNo
includeHashtagsNo
numberOfHashtagsNo
numberOfVariationsNo
customPromptModifierNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.2

TDQS

C2.7/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full behavioral burden. It usefully discloses a cost signal (~2 credits), which is real added value, but it says nothing about authentication, rate limits, whether the image URL is fetched/stored, per-call latency, or failure modes for a 14-parameter remote generation tool.

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?

A single front-loaded sentence naming the action, outputs, input medium, and cost. It is efficient with no filler, though the trailing cost parenthetical is the only structured detail in an otherwise very short definition.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a 14-parameter tool with zero schema coverage, no annotations, and no output schema, the description is far too thin. It does not explain what the response contains, how outputType interacts with captionLength/hashtags, or how many variations are returned, leaving the agent to guess at invocation behavior.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0% across 14 parameters, so the description needed to compensate and does not; it explains none of tone, platform, outputType, nsfwFilter, or the credit-affecting knobs. The enum values and defaults in the schema convey partial meaning on their own, but the description adds nothing beyond what the raw schema already shows.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb (generate) and concrete outputs (captions, social media posts, AI prompts) derived from an image, which is clear and actionable. It overlaps conceptually with siblings like aaddyy_instagram_post_generator and aaddyy_prompt_maker, but it does not explicitly differentiate itself from them.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description offers no guidance on when to choose this tool over the many overlapping sibling generators (instagram/linkedin/thread post generators, prompt_maker, image_prompt_creator). It implies the tool is for image-to-text generation but gives no conditions, prerequisites, or exclusions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.