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Generate an image with your own account

image_generate

Generates and saves full-quality images into a project using multiple AI providers without API keys, with automatic fallback if a provider fails.

Instructions

Generates one image (Flow and Higgsfield can return several) and saves the full-quality file inside the project. Providers: codex (ChatGPT plan via Codex CLI, most reliable), chatgpt (chatgpt.com), flow (Google Flow, Nano Banana 2, usually 0 credits), gemini (gemini.google.com), higgsfield (official CLI, costs credits: quote first), plus any JSON-defined site (call providers_list). Without output_path the file goes to /.ai-media//. Look at the result yourself before using it. Pass fallback_providers to try others in order if the first one fails.

Input Schema

NameRequiredDescriptionDefault
countNoFlow only
modelNoHiggsfield only, e.g. nano_banana_2, gpt_image_2_5
aspectNo
promptYes
qualityNoFlow onlyoriginal
providerYes
overwriteNo
max_creditsNoHiggsfield only: quote first, then cap
output_pathNoRelative to the project, e.g. public/images/hero.png
reference_imagesNoCodex only: up to 4 images inside the project to use as references, for example a clear photo of the person whose face must stay recognisable. Only use a photo of someone who has said yes (the user's own, or a person who agreed). The description says what to keep and what to change.
fallback_providersNoTry these in order if the first provider fails (for example a site redesign broke a selector). Each attempt and why it failed is reported in the result.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.4.1

TDQS

A4.3/5.0
Behavior4/5

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

With no annotations, the description carries the burden and does real work: it discloses the default output location (<project>/.ai-media/<provider>/), the credit-cost implication of higgsfield and the max_credits cap, provider reliability ranking, and that failed fallback attempts are reported. Still missing permission/account prerequisites (the accounts_login / accounts_status siblings hint there are auth steps) and overwrite behavior.

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?

Front-loaded with the core action and effect, then operational details. Dense with parentheticals but nearly every clause carries decision-relevant information; the one instructional aside ('Look at the result yourself') is short and useful.

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?

For an 11-parameter, no-annotation, no-output-schema tool, the description covers the hard parts: provider selection, cost gating, default save location, and retry semantics. Gaps remain on auth prerequisites, overwrite semantics, and the shape of the returned result beyond 'attempts and failures are reported'.

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 64%, and the description compensates where the schema is silent: the provider parameter has no schema description, but the text enumerates codex, chatgpt, flow, gemini, higgsfield and JSON-defined sites with traits and cost notes, and it explains the output_path default location. It does not add meaning for aspect, model, overwrite, or count beyond what the schema already says.

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?

States a specific verb and resource (generate one image) plus the side effect (saves the full-quality file inside the project), which cleanly separates it from video_generate and video_edit among the siblings. It even flags that Flow and Higgsfield can return several images despite the singular name.

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?

Gives concrete operating guidance: quote before spending credits on higgsfield, call providers_list for JSON-defined sites, use fallback_providers with ordered retries, and inspect the result before using it. It stops short of explicit when-not-to-use routing (e.g. versus video_generate or asking the user for a provider preference), so it is clear context rather than full alternatives coverage.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.