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post-txt2img-ip-adapter-inferences

Generate images from text prompts using a reference image to guide style or character consistency in the Scenario.com MCP Server.

Instructions

Trigger a new image generation in Txt2Img mode with one IpAdapter reference image that guides the generation process.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
originalAssetsNoIf set to true, returns the original asset without transformation
dryRunNo
ipAdapterImageIdsNo
ipAdapterImageIdNoDeprecated for type txt2img-ip-adapter and img2img-ip-adapter, use `ipAdapterImageIds` instead. The IpAdapter image as an AssetId. Cannot be set if `ipAdapterImage` is provided. Will be ignored if the `ipAdapterImageIds` parameter is provided.
ipAdapterScaleNoDeprecated for type txt2img-ip-adapter and img2img-ip-adapter, use `ipAdapterScales` instead. IpAdapter scale factor (within [0.0, 1.0], default: 0.9). Will be ignored if the `ipAdapterScales` parameter is provided
seedNoUsed to reproduce previous results. Default: randomly generated number.
modelIdYesThe model id to use for the inference
ipAdapterTypeNoThe type of IP Adapter model to use. Must be one of [`style`, `character`], default to `style``
modelEpochNoThe epoch of the model to use for the inference. Only available for Flux Lora Trained models.
hideResultsNoIf set, generated assets will be hidden and not returned in the list of images of the inference or when listing assets (default: false)
aspectRatioNoThe aspect ratio of the generated images. Only used for the model flux.1.1-pro-ultra. The aspect ratio is a string formatted as "width:height" (example: "16:9").
ipAdapterScalesNo
ipAdapterImageNoDeprecated for type txt2img-ip-adapter and img2img-ip-adapter, use `ipAdapterImages` instead. The IpAdapter image as a data url. Will be ignored if the `ipAdapterImages` parameter is provided.
negativePromptNoThe prompt not to guide the image generation, ignored when guidance < 1 (example: "((ugly face))") For Flux based model (not Fast-Flux): requires negativePromptStrength > 0 and active only for inference types txt2img / img2img / controlnet.
schedulerNoThe scheduler to use to override the default configured for the model. See detailed documentation for more details.
intermediateImagesNoEnable or disable the intermediate images generation (default: false)
conceptsNo
guidanceNoControls how closely the generated image follows the prompt. Higher values result in stronger adherence to the prompt. Default and allowed values depend on the model type: - For Flux dev models, the default is 3.5 and allowed values are within [0, 10] - For Flux pro models, the default is 3 and allowed values are within [2, 5] - For SDXL models, the default is 6 and allowed values are within [0, 20] - For SD1.5 models, the default is 7.5 and allowed values are within [0, 20]
numInferenceStepsNoThe number of denoising steps for each image generation (within [1, 150], default: 30)
numSamplesNoThe number of images to generate (within [1, 128], default: 4)
ipAdapterImagesNo
widthNoThe width of the generated images, must be a 8 multiple (within [64, 2048], default: 512) If model.type is `sd-xl`, `sd-xl-lora`, `sd-xl-composition` the width must be within [512, 2048] If model.type is `sd-1_5`, the width must be within [64, 1024] If model.type is `flux.1.1-pro-ultra`, you can use the aspectRatio parameter instead
negativePromptStrengthNoOnly applicable for flux-dev based models for `txt2img`, `img2img`, and `controlnet` inference types. Controls the influence of the negative prompt. Default 0 means the negative prompt has no effect. Higher values increase negative prompt influence. Must be > 0 if negativePrompt is provided.
baseModelIdNoThe base model to use for the inference. Only Flux LoRA models can use this parameter. Allowed values are available in the model's attribute: `compliantModelIds`
promptYesFull text prompt including the model placeholder. (example: "an illustration of phoenix in a fantasy world, flying over a mountain, 8k, bokeh effect")
heightNoThe height of the generated images, must be a 8 multiple (within [64, 2048], default: 512) If model.type is `sd-xl`, `sd-xl-lora`, `sd-xl-composition` the height must be within [512, 2048] If model.type is `sd-1_5`, the height must be within [64, 1024] If model.type is `flux.1.1-pro-ultra`, you can use the aspectRatio parameter instead
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool 'triggers a new image generation,' implying a write operation that creates assets, but doesn't disclose critical behaviors like whether this is an asynchronous job, what permissions are required, rate limits, or how results are returned (e.g., as asset IDs or direct images). The description lacks details on error handling, cost implications, or any side effects, which are essential for a generative tool with many parameters.

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, efficient sentence that front-loads the core purpose without any wasted words. It directly states the action and key feature (IpAdapter reference image), making it easy to parse quickly. Every part of the sentence earns its place by conveying essential information in a compact form.

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?

Given the complexity of the tool (26 parameters, no annotations, no output schema), the description is insufficiently complete. It doesn't address the tool's behavior, output format, error conditions, or integration with other tools. For a generative AI tool with many configuration options and no structured output documentation, the description should provide more context about what happens after triggering (e.g., returns a job ID or assets) and how to handle the results, which is currently missing.

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 description coverage is high at 81%, meaning most parameters are well-documented in the schema itself. The description adds minimal value beyond the schema by mentioning 'one IpAdapter reference image,' which hints at the 'ipAdapterImageIds' or 'ipAdapterImages' parameters but doesn't explain their semantics further. Since the schema does the heavy lifting, the baseline score of 3 is appropriate, as the description doesn't significantly enhance parameter understanding.

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?

The description clearly states the action ('Trigger a new image generation') and the mode ('Txt2Img mode with one IpAdapter reference image'), which specifies the verb and resource. It distinguishes itself from generic txt2img tools by mentioning the IpAdapter reference image, but it doesn't explicitly differentiate from sibling tools like 'post-img2img-ip-adapter-inferences' or 'post-controlnet-ip-adapter-inferences', which have similar IpAdapter functionality but different modes.

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 when to choose txt2img-ip-adapter over img2img-ip-adapter, controlnet-ip-adapter, or other image generation tools in the sibling list. There's no context about prerequisites, such as needing an existing IpAdapter image ID or understanding of the IpAdapter mechanism, leaving the agent without usage direction.

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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