Create multiple images concurrently from text prompts using Replicate API. Set dimensions, model, negative prompt, and inference steps for each batch.
Generate multiple images concurrently from text prompts.
| Name | Required | Description | Default |
|---|---|---|---|
| width | No | ||
| height | No | ||
| prompts | Yes | ||
| model_name | No | ||
| guidance_scale | No | ||
| negative_prompt | No | ||
| num_inference_steps | No |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections.
v1.0.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full behavioral burden, yet it discloses almost nothing beyond 'concurrently'. It omits whether concurrency is bounded, rate limits, cost, partial-failure handling, or whether all prompts use identical width/height/model settings. The single adjective 'concurrently' is the only behavioral hint.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
A single tight sentence that front-loads the verb and resource with zero filler. It is appropriately sized, though its brevity is partly the cause of the missing parameter and behavioral detail rather than pure economy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
An output schema exists, so return values need not be explained, but for a 7-parameter generative tool with no annotations the description is far too thin. It leaves key operational facts (per-prompt vs global settings, concurrency limits, failure behavior) undefined for an agent to call it correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Seven parameters with 0% schema description coverage, so the description is responsible for compensating and it does not name a single parameter. It never explains that prompts is an array of independent prompts, nor what guidance_scale, num_inference_steps, model_name, or negative_prompt control. The only inference available is that width/height likely apply globally to the batch.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
States a specific verb and resource ('Generate multiple images') with a clear scope qualifier ('concurrently from text prompts') that implicitly distinguishes it from the single-image sibling generate_image. The plural 'multiple' and 'batch' naming make the batching purpose obvious, though no sibling is named explicitly.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Gives no when-to-use guidance. It never says to prefer this over generate_image when generating several prompts, nor does it describe any conditions, prerequisites, or exclusions relative to generate_from_reference_image or edit_image. The batch intent is inferable only from the name and the word 'concurrently'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.