ai_image_proGenerate a hi-res 2K image with Nano Banana Pro — best for text-in-image & infographics. ~$0.18.
| Name | Required | Description | Default |
|---|---|---|---|
| prompt | Yes | What to draw | |
| aspect_ratio | No | 1:1 | 16:9 | 9:16 | 4:3 | 3:4 (default 1:1) |
| 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 not a read-only operation (readOnlyHint=false). The description adds valuable behavioral context beyond annotations by disclosing the output resolution (2K), the specific model (Nano Banana Pro), and the cost (~$0.18), which are important side-effect and resource-usage details.
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?
The description is a single, front-loaded sentence that captures the essential action, use case, and cost. Every phrase contributes value, with no redundancy or filler.
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 tool with only two parameters, full schema coverage, and an output schema, the description is remarkably complete. It specifies the tool's purpose, its unique selling point, the output resolution, and the cost, leaving no significant gaps for an agent to select and invoke the tool correctly.
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 provides 100% coverage for both parameters: prompt ("What to draw") and aspect_ratio (with allowed values and default). The description adds no parameter-specific meaning, so it meets the baseline of 3 as defined when schema coverage is high.
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 function with a specific verb and resource: "Generate a hi-res 2K image". It also names the model (Nano Banana Pro) and highlights its differentiator ("best for text-in-image & infographics"), which distinguishes it from sibling image generation tools like ai_image, ai_image_flux, and ai_image_gpt.
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?
The description provides clear context for when to use this tool: it is positioned as the best option for text-in-image and infographics. It does not explicitly state when NOT to use it or name alternative tools, but the use-case guidance is strong enough for an agent to infer the appropriate scenario.
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.