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BlockRunAI

BlockRun MCP

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by BlockRunAI
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TypeScript
Hybrid

blockrun_image

Generate or edit images using multiple AI models. Supports text-to-image, img2img, multi-image fusion, and inpainting. Payments processed with USDC on Base or Solana.

Instructions

Generate or edit images via BlockRun. Pays with USDC on the ACTIVE chain — Base or Solana (see blockrun_wallet) — no separate API keys needed.

Actions:

  • generate (default): Create image from text prompt

  • edit: Transform an existing image using img2img

Generation models (1024x1024 base price; larger sizes cost more on gpt-image-*):

  • openai/gpt-image-2 ($0.06–0.12) — flagship, reasoning-driven, multilingual on-image text + character consistency (default)

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

  • google/nano-banana ($0.05) — Gemini-family image model

  • google/nano-banana-pro ($0.10; $0.15 at 4096px) — up to 4K, strongest photorealism

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

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

  • zai/cogview-4 ($0.015) — cheapest, photorealistic detailed scenes

Edit (img2img) models: openai/gpt-image-2 (default), openai/gpt-image-1, google/nano-banana, google/nano-banana-pro Multi-image edit: pass an array of 2–4 source images to "image" to fuse them in one render (openai/* up to 4, google/* up to 3) — e.g. a subject plus a sprite layout guide, or a reference plus a brand logo. Source images and masks accept a base64 data URI, an http(s) URL, or a local file path (auto-encoded). Inpaint mask (openai/gpt-image-* only) via "mask"; not combinable with multiple source images.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
maskNoInpaint mask for edit action (openai/gpt-image-* only): a base64 data URI, http(s) URL, or local file path. Transparent areas of the mask are regenerated. Cannot be combined with multiple source images.
sizeNoImage size. Common values: 1024x1024 (all models), 1536x1024 / 1024x1536 (gpt-image-*), 2048x2048 / 4096x4096 (nano-banana-pro)1024x1024
imageNoSource image(s) for edit action: a base64 data URI, an http(s) URL, or a local file path (auto-encoded to a data URI) — or an array of 2–4 to fuse into one render (e.g. subject + layout guide, or reference + brand logo). openai/* accepts up to 4, google/* up to 3; a mask cannot be combined with multiple images.
modelNoModel to use (default: openai/gpt-image-2 for both generate and edit). gpt-image-2 renders on-image text best; nano-banana-pro for 4K photorealism; cogview-4 / grok-imagine-image for cheap drafts.
actionNogenerate: create from text; edit: transform existing imagegenerate
inlineNoReturn a small inline image preview (thumbnail) the client can render in-conversation, in addition to the full-resolution URL. Defaults to the BLOCKRUN_INLINE_IMAGES env setting (off unless set). Rich clients (e.g. the VS Code extension) render it; plain terminals ignore it. Off keeps responses lightweight.
promptYesImage description or edit instructions
qualityNostandard
agent_idNoAgent identifier for budget tracking and enforcement.

Tool Definition Quality

A4.1/5.0
Behavior3/5

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

No annotations are provided, so the description must fully disclose behavior. It covers payment mechanism, model costs, actions, multi-image editing, and inline preview behavior. However, it does not mention authentication requirements, rate limits, error handling, or potential destructive effects of editing (e.g., overwriting original images). This is a moderate gap for a tool with no annotations.

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 well-structured with clear sections (Actions, Generation models, Edit models, Multi-image edit, etc.). It is front-loaded with the main purpose and payment method. Each part serves a purpose, though it is relatively long; however, the length is justified by the tool's complexity (9 parameters, multiple models/actions).

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

Completeness4/5

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

Given the tool's complexity (9 parameters, 3 enums, multiple models and actions), the description covers actions, model selection with cost and use cases, input formats, inline preview, and multi-image editing. It lacks an explicit description of the output format (only mentions return of URL and optional preview), but for an image generation tool, this is acceptable. Overall, it provides sufficient context for an agent to use the tool effectively.

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?

With 89% schema description coverage, the baseline is high. The description adds significant value by explaining model selection criteria (e.g., 'gpt-image-2 renders on-image text best'), multi-image editing details (array fusion, model limits), input formats (base64, URL, file path), and inline behavior. This goes beyond the schema's descriptions, enhancing the agent's understanding.

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 'Generate or edit images via BlockRun' and details two actions (generate, edit), distinguishing it from sibling tools like blockrun_chat or blockrun_markets. It specifies the resource (images) and verb, making the purpose unambiguous.

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 different models (e.g., gpt-image-2 for on-image text, nano-banana-pro for 4K photorealism) and mentions payment via USDC on the active chain. It does not explicitly exclude alternatives, but the sibling tool names (e.g., blockrun_chat, blockrun_video) imply the image tool is for image tasks, which is sufficient context.

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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