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BlockRunAI

BlockRun MCP

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by BlockRunAI

blockrun_image

Generate or edit AI images using multiple models. Pays with USDC on Base or Solana — no separate API keys required.

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.
Behavior4/5

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

Annotations indicate readOnlyHint=false, destructiveHint=false, and openWorldHint=false. The description adds behavior beyond annotations, such as payment details, file path auto-encoding, and limitations on mask usage with multiple images. No contradiction found.

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?

The description is well-structured with clear sections for actions, models, and parameter details. It is concise yet comprehensive, using bullet points for readability without wasted words.

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?

The description covers all necessary functionality for an agent to use the tool effectively, including payment, model choices, and parameter constraints. No output schema exists, but the description explains return values (image URL, optional inline preview) adequately.

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 89% (high), and the description adds significant meaning to parameters, e.g., model details, multi-image edit capabilities, and inline preview behavior. It enriches the schema without redundancy.

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 generates or edits images via BlockRun with specific actions (generate/edit) and models. It distinguishes from sibling tools by focusing on image generation/editing, while siblings like blockrun_music and blockrun_video handle other media.

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?

The description guides when to use generate vs edit, provides model recommendations, and mentions payment via USDC on the active chain. It does not explicitly state when not to use the tool, but the context of siblings implies alternatives for other tasks.

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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