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runapi-ai

@runapi.ai/flux-kontext-mcp

by runapi-ai

text_to_image

Generate images from text prompts using Flux Kontext models. Returns task ID, status, and output URLs.

Instructions

Create a Flux Kontext task on RunAPI (text to image). Returns a task id, status, and output URLs.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
promptNo
watermarkNo
aspect_ratioNo
callback_urlNo
output_formatNo
safety_toleranceNo
source_image_urlNo
enable_translationNo
enable_prompt_expansionNo
waitNoPoll until the task reaches a terminal status.
timeout_msNo
poll_interval_msNo
modelNoRunAPI model slug for this model line.
Behavior2/5

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

With no annotations, the description carries full burden but only mentions a task creation and return values. It does not disclose behavioral traits like async nature, failure modes, rate limits, auth requirements, or potential destructive actions.

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 a single concise sentence that effectively front-loads the core purpose and output. However, it is slightly too brief for a tool with 13 parameters.

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

Completeness1/5

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

The tool has 13 parameters and no output schema, yet the description provides no details on how parameters affect behavior, async execution, or error handling. It is completely insufficient 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.

Parameters1/5

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

Schema coverage is only 15%, with only 2 of 13 parameters having descriptions. The description adds no parameter details, leaving an agent without understanding of crucial parameters like 'prompt', 'aspect_ratio', 'model', etc.

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 the tool creates a Flux Kontext task for text-to-image generation and specifies the return values (task id, status, output URLs). It distinguishes from sibling tools 'check_pricing' and 'get_task' which have different purposes.

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, no prerequisites, and no when-not-to-use conditions. It merely states what it does.

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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