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 this is a non-read-only, non-destructive operation. The description adds valuable behavioral context: approximate cost (~$0.07) and output format (returns PNG URLs), which helps the agent set expectations about the result.
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?
Two concise sentences communicate the core purpose, cost, and output format with zero unnecessary detail. The information is front-loaded and easy to parse.
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?
For a simple two-parameter tool with an output schema, the description covers the essentials: what it does, cost, and return type. It could mention any prerequisites (e.g., API key) or rate limits, but annotations address safety, so the description is sufficiently complete.
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?
The input schema already documents both parameters with high coverage (100%). The prompt description adds a small nuance ('English works best'), but the aspect_ratio list is fully covered in the schema, and the description doesn't provide additional semantic meaning beyond structured fields.
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?
The description clearly states the tool's purpose with a specific verb ('Generate') and resource ('AI image with OpenAI GPT Image 2'). It distinguishes from sibling image tools by naming the exact model (GPT Image 2), unlike ai_image_flux or 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?
There is no guidance on when to use this tool versus alternatives. The description doesn't reference any sibling tools or provide exclusion criteria, despite the context showing multiple related image tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools have clearly distinct purposes (image, music, video, vision, voice, etc.), but some overlap exists: ask_ai vs ask_ai_pro differ only in model strength, and web_search vs research_report both involve search with AI responses. Descriptions help clarify, though an agent could misselect in edge cases.
Tool names follow a mostly consistent snake_case pattern, with many using an 'ai_' prefix for generation tasks. However, name styles vary between verb_noun (call_endpoint, remove_bg) and noun_verb (crypto_prices, domain_info), and ask_ai/ask_ai_pro break the ai_ prefix convention. Minor deviations, but the overall pattern is readable.
At 16 tools, the server is slightly above the ideal 3-15 range but remains well-scoped for a multi-purpose utility server. Each tool has a distinct function, and the count feels manageable rather than overwhelming.
The server covers a broad set of capabilities (AI generation, web search, crypto, domain info), but it lacks lifecycle management for generated assets—there are no list/get/delete operations for previously created media, and the domain appears to be a collection of paid endpoints rather than a cohesive service.