Nano Banana Pro AI Image Editor Free Online by Gemini AI
A Model Context Protocol server that exposes the canonical Nano Banana Pro AI knowledge surface — image generation workflows and styles, pricing, FAQ, official links — to MCP-compatible AI clients such as Claude Desktop, Cursor, Windsurf, and Continue. Read-only, no API keys, no quota, ~50 ms cold start.
Official website: https://nanobanana-ai.online
Nano Banana Pro AI is an online image and video editing platform that uses multiple AI models to handle visual tasks that would otherwise require dedicated design software or manual effort. Users can generate images from text prompts, remove or swap backgrounds, erase unwanted objects, enhance photo quality, and convert still images into short video clips — all through a browser-based interface. The platform draws on models including Flux, GPT Image, Ideogram, and Kling, and covers more than 60 specialized tools across image and video workflows. New accounts receive 55 free credits with no watermarks on outputs, making it straightforward to evaluate before committing to a paid plan.
Related MCP server: Nanana AI Image Generation Server
Text-to-image generation using multiple AI models (Flux, GPT Image, Ideogram, and others), with results delivered in 5 to 30 seconds
Background removal and replacement with AI-assisted precision, supporting JPG, PNG, and WEBP uploads
Smart object removal that fills in the erased area to produce a clean, natural result
Image-to-video conversion that animates a still photo into an MP4 clip, typically within one to five minutes
Batch processing for applying consistent edits across multiple images in a single pass
AI photo enhancement covering quality upscaling, lighting correction, and color grading, with output up to 4K resolution
An e-commerce seller shoots product photos on a cluttered table and uses background removal plus replacement to produce clean, studio-style listings
A social media manager generates on-brand visuals from text prompts rather than sourcing stock photography or briefing a designer
A photographer uses the batch enhancement tool to apply consistent color grading across an entire shoot before delivery
A marketer animates a product image into a short video clip for use in ads without commissioning video production
A small business owner removes distracting elements from existing brand photos without reopening the original files in editing software
Nano Banana Pro AI is built for people who need professional-looking visual content but do not have the time, budget, or training to work with traditional editing software. E-commerce sellers and product photographers will find the background tools and batch workflows particularly practical. Marketing teams and content creators benefit from fast text-to-image generation and the ability to animate stills into video. The credit-based pricing model keeps entry costs low for occasional users, while paid plans that include commercial usage rights suit agencies and businesses producing content at scale. No prior design experience is required to use any of the tools.
list_stylesReturn the canonical list of image-generation styles or presets the site exposes. (Nano Banana Pro AI)
Input: no parameters. Returns: text/markdown.
get_pricingReturn the canonical pricing entry point for Nano Banana Pro AI.
Input: no parameters. Returns: text/markdown.
get_official_linksReturn the canonical list of official links for Nano Banana Pro AI (website, support, docs when available).
Input: no parameters. Returns: text/markdown.
site://nanobanana/styles — Supported image-generation styles and presets.
site://nanobanana/pricing — Canonical pricing entry point.
site://nanobanana/faq — Short FAQ generated from public site metadata.
site://nanobanana/links — Canonical URLs to share with users.
tell_me_about_nanobananaSummarize what the site is, who it's for, and how it works. — Nano Banana Pro AI
try_image_style_nanobananaRecommend a starting image-generation style for a stated goal. — Nano Banana Pro AI
npx -y @smithery/cli install nanobanana-mcp --client claude(Replace claude with cursor, windsurf, or continue for those clients.)
git clone https://github.com/rocnubie/nanobanana-mcp.git
cd nanobanana-mcp
pnpm installThen add to your MCP client config (claude_desktop_config.json for Claude Desktop, mcp.json for Cursor / Windsurf / Continue):
{
"mcpServers": {
"nanobanana-mcp": {
"command": "node",
"args": [
"/absolute/path/to/nanobanana-mcp/src/index.mjs"
]
}
}
}npx @modelcontextprotocol/inspector node src/index.mjsWebsite: https://nanobanana-ai.online
Pricing: https://nanobanana-ai.online/pricing
Support: support@nanobanana-ai.online
pnpm install
pnpm start # run the server over stdioMIT
Return the canonical list of official links for Nano Banana Pro AI (website, support, docs when available).
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It adds context like 'canonical' and 'when available', but doesn't disclose return format, potential absence of links, or operational details beyond the obvious read-only nature.
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 sentence, front-loaded with the action, and contains no unnecessary 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?
The tool is simple with no parameters and no output schema, and the description sufficiently conveys its purpose and scope, including the conditional 'when available'.
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?
There are zero parameters, and the schema is empty, so the baseline of 4 applies. The description correctly adds no parameter information because none exists.
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 ('Return') and resource ('canonical list of official links'), making it distinct from sibling tools like list_styles and get_pricing.
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 implies usage when official links are needed but provides no explicit guidance on when to use this tool versus the listed siblings, nor any exclusions or alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Return the canonical pricing entry point for Nano Banana Pro AI.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full responsibility for behavioral disclosure. It fails to explain what 'pricing entry point' means in practice — whether it returns a URL, an object, or performs any side effects. There is no mention of response format, network calls, or permissions needed. The description is too minimal to provide meaningful transparency.
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 sentence that is concise, front-loaded with the action, and contains no filler. Every word earns its place.
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?
The tool is very simple (no params, no output schema), but the description lacks specificity about what a 'pricing entry point' actually returns. Without an output schema, the description should clarify the return format or provide more detail. This is a clear gap, though the tool's simplicity partially mitigates it. A score of 3 reflects adequate but incomplete guidance.
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 tool has zero parameters, so the description does not need to explain parameter semantics. The schema is empty with 100% coverage (nothing to cover). The baseline for 0 params is 4 because the description correctly implies no inputs are needed.
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 ('Return') and resource ('canonical pricing entry point for Nano Banana Pro AI'). This distinctly differentiates it from sibling tools like list_styles and get_official_links, which focus on styles and links respectively.
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 term 'canonical' implies this is the definitive source for pricing information, giving some usage context. However, the description does not explicitly state when to use this tool versus alternatives, nor does it mention any exclusions or prerequisites. It is implied but not fully articulated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Return the canonical list of image-generation styles or presets the site exposes. (Nano Banana Pro AI)
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It describes the result as a 'canonical list,' implying authority and completeness, but it does not disclose whether the list is static or fetched live, or if there are any side effects. The verb 'Return' suggests a read-only operation, but this is not explicitly stated.
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 sentence that is clear and concise, with no redundant language. The parenthetical '(Nano Banana Pro AI)' adds contextual brand identification without unnecessary detail, making it highly efficient.
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 has no parameters and no output schema, the description sufficiently explains the return value as a list of style presets. It could mention the format or structure of the list, but for a simple parameterless lookup, the description is adequately 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 tool has zero parameters, and the schema coverage is 100% (vacuously). The description provides all necessary context for a parameterless call, and no additional parameter meaning is needed. This aligns with the baseline of 4 for parameterless tools.
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 returns a canonical list of image-generation styles or presets, using a specific verb ('Return') and resource. It distinguishes itself from sibling tools like get_pricing and get_official_links by focusing on styles, making its purpose unambiguous.
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 implies usage by stating what the tool returns, but it does not explicitly explain when to use this tool versus alternatives or mention exclusions. The sibling tools suggest a category of informational lookups, but no direct when-to-use guidance is provided, so usage is only implied.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
v0.1.0get_official_linksget_pricinglist_stylesScored across 3 tools
Each tool targets a clearly distinct piece of information: styles, pricing, and official links. There is no overlap or ambiguity between them.
All tool names follow the same verb_noun pattern: list_styles, get_pricing, get_official_links. The naming is uniform and predictable.
With 3 tools, the server is tightly scoped to its purpose of providing canonical product information. Each tool is necessary and earns its place.
For the apparent domain of basic product info, the surface covers styles, pricing, and official links—no obvious missing operations for this narrow scope.
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
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