xpay✦ Marketing Collection

ideogram_v3

Ideogram v3 Quality — AI image generation with best-in-class text rendering

Input Schema

TableJSON Schema

Name	Required	Description	Default
`mask`	No	A black and white image. Black pixels are inpainted, white pixels are preserved. The mask will be resized to match the image size.
`seed`	No	Random seed. Set for reproducible generation
`image`	No	An image file to use for inpainting. You must also use a mask.
`prompt`	Yes	Text prompt for image generation
`resolution`	No	Resolution. Overrides aspect ratio. Ignored if an inpainting image is given.	None
`style_type`	No	The styles help define the specific aesthetic of the image you want to generate.	None
`aspect_ratio`	No	Aspect ratio. Ignored if a resolution or inpainting image is given.	1:1
`style_preset`	No	Apply a predefined artistic style to the generated image (V3 models only).	None
`magic_prompt_option`	No	Magic Prompt will interpret your prompt and optimize it to maximize variety and quality of the images generated. You can also use it to write prompts in different languages.	Auto
`style_reference_images`	No	A list of images to use as style references.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

First observedMar 14, 2026

TDQS

B3.3/5.0

Behavior2/5

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

With no annotations provided, the description carries full responsibility for behavioral disclosure. It only claims 'best-in-class text rendering', which is a quality assertion, not a behavioral detail. It does not mention that the tool generates images (obvious from name), returns a URL, requires authentication, supports inpainting, or has any side effects. The schema notes some parameter interdependencies but the description itself lacks meaningful behavioral transparency.

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 a single, front-loaded sentence. It efficiently conveys the tool's purpose and a key selling point without any wasted words. This is appropriately concise and well-structured.

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

Completeness3/5

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

While the tool is complex (10 parameters, inpainting, style presets, etc.) and there is no output schema, the input schema is richly described. The one-line description does not provide usage context, return format, or examples, but the schema compensates for parameter details. Overall, the description is barely adequate for such a complex tool; it needed at least a brief mention of example usage or output expectations.

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?

Schema description coverage is 100%, so the baseline is 3. The description adds no parameter-level meaning beyond what the schema already provides. Each parameter is well-documented with its own description, so the tool description's lack of parameter details is acceptable.

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 'AI image generation', specifying the verb (generation) and resource (images). It also highlights a key differentiator ('best-in-class text rendering'), which is a specific capability. Although no explicit sibling differentiation is provided, none of the sibling tools are image generators, so the purpose is unambiguous.

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 provides no guidance on when to use this tool versus alternatives. It doesn't mention any prerequisites, use cases, or exclusions. All sibling tools are data-retrieval/scraping tools, but no explicit 'use this for image generation' contextual guidance is given.

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

C2/5.0

Disambiguation2/5

The tool set has significant overlap and ambiguity, particularly within the 'scrapecreators_' prefix where many tools appear to target similar social media platforms and content types (e.g., 'scrapecreators_posts', 'scrapecreators_posts_get', 'scrapecreators_post', 'scrapecreators_post_get'). Additionally, tools like 'tavily_research' and 'tavily_search' have overlapping purposes with 'web_search_exa', making it difficult for an agent to distinguish when to use each. While some tools like 'get_credits' or 'ideogram_v3' are distinct, the overall set is confusing due to redundant functionalities.

Naming Consistency2/5

Naming conventions are highly inconsistent across the tool set. There is a mix of snake_case (e.g., 'get_credits'), kebab-case (e.g., 'find-hooks'), and verbose prefixes (e.g., 'scrapecreators_'). The 'scrapecreators_' tools themselves vary in structure, with some using underscores and others not, and there are duplicate names with slight variations (e.g., 'scrapecreators_ad_details' vs. 'scrapecreators_ad_details_get'). This lack of a predictable pattern makes the tool set chaotic and hard to navigate.

Tool Count1/5

With 124 tools, the count is extremely high and inappropriate for the server's purpose, which appears to be marketing and social media data collection. This many tools suggests poor scoping, likely due to redundancy (e.g., multiple scraping tools for similar platforms) and overlapping functionalities. A well-scoped server in this domain should have far fewer tools, typically in the range of 10-30, to avoid overwhelming agents and ensure clarity.

Completeness4/5

Despite the high tool count and redundancy, the server covers a broad range of marketing-related functions comprehensively. It includes tools for social media hooks, content validation, SEO analysis (e.g., backlinks, keywords), voice archetypes, copywriting frameworks, and extensive scraping across multiple platforms. There are no obvious major gaps for the marketing domain, as it supports data gathering, content creation, and analysis across various networks and metrics, allowing agents to perform core marketing workflows effectively.

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