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BlockRunAI

BlockRun MCP

Official
by BlockRunAI

blockrun_image

Generate images from text prompts or edit existing images. Pay per call using USDC, no separate API keys required.

Instructions

Generate or edit images via BlockRun. Pays with USDC — no separate API keys needed.

Actions:

  • generate (default): Create image from text prompt

  • edit: Transform an existing image using img2img

Generation models:

  • zai/cogview-4 ($0.015) — Zhipu CogView-4, photorealistic, great for detailed scenes

  • xai/grok-imagine-image ($0.02) — xAI Grok Imagine, stylized, fast

  • xai/grok-imagine-image-pro ($0.07) — xAI Grok Imagine Pro, higher quality

  • openai/gpt-image-1 ($0.02-0.04) — GPT native image generation

  • openai/gpt-image-2 ($0.06-0.12) — ChatGPT Images 2.0, reasoning-driven, multilingual text rendering + character consistency

  • openai/dall-e-3 ($0.04-0.08) — High quality, prompt adherence

  • google/nano-banana ($0.05) — Google image model Edit models: openai/gpt-image-1, openai/gpt-image-2 (default for edits)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
promptYesImage description or edit instructions
actionNogenerate: create from text; edit: transform existing imagegenerate
modelNoModel to use (default: dall-e-3 for generate, gpt-image-2 for edit). xai/grok-imagine-image is stylized and fast; xai/grok-imagine-image-pro is higher quality; gpt-image-2 is the newest edit-capable model with stronger instruction following.
imageNoSource image for edit action: base64-encoded image or URL
sizeNo1024x1024
qualityNostandard
agent_idNoAgent identifier for budget tracking and enforcement.
Behavior3/5

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

No annotations provided; description carries full burden. Discloses payment method, actions, and default models, but omits details on rate limits, error handling, or output format. Adequate but not exhaustive.

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?

Front-loaded with purpose and payment, then structured list of actions and models. Every sentence is meaningful; no redundancy.

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?

No output schema; description does not explain return format (e.g., image URL). Missing details on budget enforcement beyond agent_id. Adequate for a generative tool but has gaps.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

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

Schema coverage is 71%; description adds significant value for model parameter (prices, characteristics) and default behaviors for action. Other parameters have schema descriptions that are not elaborated, but the added detail for model is strong.

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?

Clearly states the tool generates or edits images, with explicit actions (generate/edit). Distinguishes from sibling tools as the only image-specific tool among them.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides guidance on when to use generate vs edit, lists models with prices and characteristics. Lacks explicit when-not-to-use or alternatives outside the tool, but context is clear.

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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