Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@bestaiimageprompt-mcp list the available image styles"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Best AI Image Prompt | GPT Image 2 & Nano Banana Prompts
A Model Context Protocol server that exposes the canonical Best AI Image Prompt 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://bestaiimageprompt.com
Best AI Image Prompt (https://bestaiimageprompt.com) is a curated gallery of tested AI image prompts, each paired with its actual rendered output. Every entry is sourced from working creators on X, Reddit, and Discord — real strings that real people pasted into real models and published. The site combines a browsable prompt library of 5,000+ entries with an in-page generation dock, so users can go from discovering a prompt to producing their own image without leaving the page. The catalog is organized into 20+ style categories, updated weekly with trending effects, and requires no account or paywall to access.
Related MCP server: prompt-enhancement-mcp-server
Curated prompt library — 5,000+ entries filtered by category (Portrait, Cinematic, 3D, Product, Anime, Cyberpunk, and more), with keyword search across styles, subjects, and effects.
Paired output previews — every prompt is shown alongside the image it actually generated, so the result is visible before copying.
One-click copy — prompts are accessible without login; a single click copies the full text ready to paste into any model.
In-page generation dock — an integrated generator auto-fills the selected prompt and lets users pick from multiple AI engines (Flux, GPT Image, Seedream, Gemini AI, Nano Banana) without switching tabs.
Engine-aware routing — each prompt is tagged with the model it was written for; the system routes to the appropriate engine automatically or allows manual override.
Per-card translation — prompts can be translated into 60+ locales while preserving model-specific syntax and trigger words.
Weekly trending drops — a curated selection of viral effects is surfaced each week, making it easy to stay current with what is performing well across social platforms.
A product photographer wants consistent studio-style backgrounds for e-commerce listings and browses the Product category to find tested prompts that match the lighting style they need.
A content creator on a deadline needs a cinematic portrait for a thumbnail and copies a proven prompt directly into the in-page generator to produce several variants in minutes.
A game developer is exploring concept art styles and uses the keyword search to find prompts tagged with specific aesthetic references, then generates variations by swapping subject descriptions.
A marketing team working across multiple regions uses the translation feature to adapt prompts into local languages without losing the trigger words that control the visual style.
A brand designer new to AI image generation wants to learn what makes a prompt work by studying real examples paired with their outputs before writing their own.
Best AI Image Prompt serves a wide range of people who work with AI-generated images but do not want to spend time on prompt engineering from scratch. That includes e-commerce founders building product photography pipelines, content creators who need visuals quickly, concept artists and game developers exploring style references, and marketing professionals producing imagery for campaigns. It is also a practical starting point for people who are new to AI image generation entirely — the paired prompt-plus-output format makes it easy to understand what each prompt does and how to adapt it, without requiring any prior experience with model syntax or parameter tuning.
list_stylesReturn the canonical list of image-generation styles or presets the site exposes. (Best AI Image Prompt)
Input: no parameters. Returns: text/markdown.
get_pricingReturn the canonical pricing entry point for Best AI Image Prompt.
Input: no parameters. Returns: text/markdown.
get_official_linksReturn the canonical list of official links for Best AI Image Prompt (website, support, docs when available).
Input: no parameters. Returns: text/markdown.
site://bestaiimageprompt/styles — Supported image-generation styles and presets.
site://bestaiimageprompt/pricing — Canonical pricing entry point.
site://bestaiimageprompt/faq — Short FAQ generated from public site metadata.
site://bestaiimageprompt/links — Canonical URLs to share with users.
tell_me_about_bestaiimagepromptSummarize what the site is, who it's for, and how it works. — Best AI Image Prompt
try_image_style_bestaiimagepromptRecommend a starting image-generation style for a stated goal. — Best AI Image Prompt
npx -y @smithery/cli install bestaiimageprompt-mcp --client claude(Replace claude with cursor, windsurf, or continue for those clients.)
git clone https://github.com/rocnubie/bestaiimageprompt-mcp.git
cd bestaiimageprompt-mcp
pnpm installThen add to your MCP client config (claude_desktop_config.json for Claude Desktop, mcp.json for Cursor / Windsurf / Continue):
{
"mcpServers": {
"bestaiimageprompt-mcp": {
"command": "node",
"args": [
"/absolute/path/to/bestaiimageprompt-mcp/src/index.mjs"
]
}
}
}npx @modelcontextprotocol/inspector node src/index.mjsWebsite: https://bestaiimageprompt.com
Community: https://x.com/bestaiimageprompt
Support: support@bestaiimageprompt.com
pnpm install
pnpm start # run the server over stdioMIT
Return the canonical list of official links for Best AI Image Prompt (website, support, docs when available).
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It describes a read operation returning a list, but does not disclose any potential side effects or safety traits. Given zero parameters and simple retrieval, this is minimally adequate.
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 sentence, front-loaded with action and object, no redundant words. Efficient and clear.
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 zero parameters, no output schema, and a simple retrieval goal, the description is mostly complete. Could optionally specify return format (e.g., JSON array), but not essential.
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 with 100% schema coverage. Description correctly has no param details. Baseline score of 4 applies as no additional value 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?
Description clearly states verb 'Return' and resource 'canonical list of official links' for a specific product. Clearly distinguishes from sibling tools (list_styles, get_pricing) which serve different purposes.
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, but the narrow scope implies usage for obtaining official links. Siblings are distinct enough that no confusion arises.
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 Best AI Image Prompt.
| 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 for behavioral disclosure. It implies a read-only operation ('Return'), which is appropriate for a pricing tool, but does not mention any limitations, authentication needs, or rate limits. The simplicity of a zero-parameter tool mitigates the need for extensive disclosure, resulting in an adequate score.
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, concise sentence that clearly communicates the tool's purpose without any extraneous information. It is appropriately front-loaded and 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, no output schema, and simple sibling context, the description is partially complete. It clearly states the tool's action but does not describe the output format or what the 'pricing entry point' exactly is (e.g., URL, string, object). This ambiguity reduces completeness.
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 no parameters in the input schema, so schema coverage is 100%. The description adds no parameter information, which is acceptable since none exist. Baseline score of 4 is appropriate as the description does not need to compensate for missing parameter details.
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 the 'canonical pricing entry point' for Best AI Image Prompt. It uses a specific verb 'Return' and specifies the resource 'pricing entry point', which distinguishes it from sibling tools like get_official_links and list_styles. However, it could be more precise about what constitutes a 'pricing entry point'.
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 no guidance on when to use this tool versus alternatives. It does not mention context, prerequisites, or when not to use it, leaving the agent to infer usage based solely on the tool's name and purpose.
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. (Best AI Image Prompt)
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must disclose behavioral traits. It states the tool returns a 'canonical list' but does not mention that it is read-only, has no side effects, or any other behavioral constraints. For a simple read operation, the description is insufficient.
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 plus a parenthetical. The sentence is clear and front-loaded. The parenthetical '(Best AI Image Prompt)' is slightly extraneous but does not harm conciseness significantly. Could be more concise by removing the parenthetical.
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, no output schema, and is a simple listing, the description is minimally complete. It identifies the content (styles/presets) but does not describe the structure of the returned list (e.g., format, fields). An AI agent could infer that the list contains style names, but more detail would improve usability.
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?
No parameters exist, so the description must add meaning about the return. It specifies the resource type (image-generation styles/presets), which provides context beyond the empty schema. Baseline is 4 for zero parameters, and the description meets that by clarifying the output domain.
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?
Verb 'Return' and resource 'canonical list of image-generation styles or presets' clearly state the tool's function. The parenthetical 'Best AI Image Prompt' adds minor context but does not obscure purpose. Siblings are unrelated, so no confusion.
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 guidance on when to use this tool versus siblings. However, given siblings are clearly different (get_official_links, get_pricing), the description implicitly suggests it is for retrieving style lists. Still, no when-not-to-use or alternative conditions.
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. Dates show when Glama detected each change.
v0.1.0get_official_linksget_pricinglist_stylesEach tool targets a distinct aspect: links, styles, and pricing. No overlap or ambiguity.
All tools follow a consistent 'verb_noun' pattern: get_official_links, list_styles, get_pricing.
Three tools is appropriate for an information server covering core aspects of the service.
Covers the main information needs (links, styles, pricing). Minor gaps like features or FAQs are acceptable.
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
Generate AI images and videos from any compatible MCP client.
Generate AI images, video, music, and sound effects, and upscale them, from any MCP client.
Use AI models for chat, image, and video generation from Claude Code and other MCP hosts.
Focused MCP server for OpenAI image/audio generation (v2.0.0). Wraps endpoints via HAPI CLI.
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