ai_image_gptGenerate an AI image with OpenAI GPT Image 2. ~$0.07. Returns PNG URL(s).
| Name | Required | Description | Default |
|---|---|---|---|
| prompt | Yes | What to draw (English works best) | |
| aspect_ratio | No | auto | 1:1 | 16:9 | 9:16 | 4:3 | 3:4 |
| Name | Required | Description | Default |
|---|---|---|---|
| result | No |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate non-read-only and non-destructive; description adds monetary cost (~$0.07) and output format (PNG URLs), which are valuable behavioral context beyond annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Single, efficient sentence that front-loads the action and packs cost and return format with no wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Simple tool with full schema and output schema; description covers action, model, cost, and return format, sufficient for selection and invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema covers 100% of parameters with clear descriptions; the tool description doesn't add parameter-specific details, hence baseline 3.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description specifies exact action (Generate), resource (AI image), and model (OpenAI GPT Image 2), differentiating from sibling tools like ai_image_flux and ai_image_pro.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit when-to-use or alternatives are mentioned; only via model name can an agent infer usage. The cost info is present but not framed as a comparative criterion.
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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Four image generation tools, three video tools, and five 'ask' tools create significant overlap. Although descriptions specify the model, an agent must carefully compare prices and capabilities to choose correctly, making misselection likely.
All tool names use snake_case, but patterns are mixed: some start with verbs (remove_bg, scrape_page), some with nouns (crypto_prices, market_snapshot), and many use ai_/ask_ prefixes. Model suffixes like flux, gpt, pro, kling are descriptive but not systematically applied.
24 tools is heavy, inflated by near-duplicate variants for image, video, and LLM queries. While the broad scope justifies a large count, the redundant tools could have been consolidated.
The toolset covers a wide range of media and data tasks: image, video, music, voice, vision, LLM, web, crypto, domain, and endpoint discovery. Notable gaps like speech-to-text or image editing exist, but the surface is fairly complete for a general-purpose media toolkit.