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MCP Flux Pro demonstrates solid definition quality with consistent tool naming, comprehensive parameter descriptions, and well-documented schemas. All 6 tools follow verb-noun naming conventions (flux_generate_image, flux_edit_image, flux_list_models, flux_list_actions, flux_get_task, flux_get_tasks_batch). Tool descriptions are substantial (100-400+ chars) and explain use cases clearly. Input schemas are fully defined with types, enums, and detailed parameter descriptions. However, output schemas are not explicitly documented in the provided source, responses are described in natural language but JSON Schema output structures are not visible. Error handling is implicit rather than explicit, with no recovery guidance patterns. Tool composition is good: image generation and editing are properly separated, task queries support both single and batch patterns.
Edit an existing image using Flux with a text prompt. This allows you to modify an existing image based on a text description. The kontext models (flux-kontext-pro, flux-kontext-max) are specifically designed for high-quality image editing and style transfer. Use this when: - You want to modify or transform an existing image - You want to change specific elements in an image - You want to apply style changes or artistic effects - You want to add, remove, or replace objects in an image For generating new images from scratch, use flux_generate_image instead. Returns: Task ID and edited image information including URLs.
Generate AI images from a text prompt using Flux. Flux is a family of fast, high-quality image generation models by Black Forest Labs. Different models offer different tradeoffs between speed, quality, and capabilities. Use this when: - You want to create new images from a text description - You need high-quality AI-generated artwork or photos - You want fast image generation with good prompt following For editing existing images, use flux_edit_image instead. Returns: Task ID and generated image information including URLs.
Query the status and result of a Flux image generation task. Use this to check if a generation is complete and retrieve the resulting image URLs and metadata. Use this when: - You want to check if an image generation has completed - You need to retrieve image URLs from a previous generation - You used async callback and want to check results later - The initial generation returned a task_id without immediate results Returns: Task status and generation result including image URLs.
Output schemas not explicitly documented. Tool descriptions state what is returned in natural language ('Task ID and generated image information including URLs') but JSON Schema output structures are not visible in source code. LLMs need formal schema to plan downstream tool calls and extract typed fields.
No explicit error handling or recovery guidance. If an image generation fails, times out, or hits a rate limit, the tool response does not guide the LLM on what to do next (retry, check status, ask user). Tool descriptions lack 'If X happens, then Y' recovery paths.
No pagination or result limits documented. flux_list_models and flux_list_actions return 'Categorized list of all tools' with no stated limit, page size param, or total count in response. If the Flux API adds 50+ models, responses could exhaust context windows.
Inferred effective spec: 2026-07-28+.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-22 | B | 76 | 2026-07-28+ | v2 |
Query multiple Flux image generation tasks at once. Efficiently check the status of multiple tasks in a single request. More efficient than calling flux_get_task multiple times. Use this when: - You have multiple pending generations to check - You want to get status of several images at once - You're tracking a batch of generations Returns: Status and result information for all queried tasks.
List all available Flux tools and their use cases. Reference guide for what each tool does and when to use it. Returns: Categorized list of all tools with descriptions.
List all available Flux models and their capabilities. Reference guide for choosing the right Flux model for your use case. Returns: Detailed list of all Flux models with descriptions and recommendations.
Async callback URLs (callback_url parameter) documented but no polling guidance. If an agent passes a callback_url, it may abandon polling flux_get_task. If callback delivery fails, there's no fallback strategy documented.
No validation or constraint documentation for 'size' parameter. Description says 'pixel dimensions' and 'aspect ratios' but does not state valid ranges (e.g. '256-1440px for flux-dev, multiples of 32'). LLM must infer constraints from examples, risking invalid requests.