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 declare readOnlyHint=false and destructiveHint=false, so the safety profile is established. The description adds valuable behavioral context by disclosing the approximate cost (~$0.18) and the 2K resolution, which are consequences an agent should know. It does not describe every detail, but with annotations present, this is sufficient.
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, compact sentence that packs model, resolution, use case, and cost into a scannable format. Every element earns its place, 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?
Given the tool's simplicity (2 parameters, output schema present, annotations provided), the description covers the essential aspects: what it does, when to use it, and cost. It does not describe return values, but the output schema handles that, so no significant gaps remain.
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 description coverage is 100%, with both 'prompt' and 'aspect_ratio' already well-documented in the input schema. The description does not add any parameter-specific semantics, so the baseline score of 3 is appropriate because the schema handles the heavy lifting.
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 uses the specific verb 'Generate' and identifies the resource as a 'hi-res 2K image' with the model 'Nano Banana Pro'. It clearly distinguishes from sibling image tools by naming the model and focusing on 'text-in-image & infographics', making the purpose unmistakable.
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 usage context by stating it is 'best for text-in-image & infographics', which guides when to choose this tool. It does not explicitly list exclusions or compare against sibling tools, but the use-case signal is strong enough for effective selection.
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.
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.